TT-Assig #4 – see attached

TT-Assig #4 – see attached
Assignment #4: Translation Technology Services Required Readings and Links: Please read Chapter 1 & 2 of of The Future of Translation Technology (See attached) Book: Sin-wai, C. (2019). The Future of Translation Technology. New York: Routledge. Please read Chapter 1 & 2, Translation and Technology Book: O’Hagan, M. (2019). The Routledge Handbook of Translation and Technology. New York: Routledge. Development and Functions of Translation Technology PPT#1 Translation Tech- Standards PPT#2 Translation Technology and Machine Translation PPT#3 Space TMS – tutorial for Translation and Localization Project Managers https://www.youtube.com/watch?v=QSrKwPsmjj8 How to translate a document in SDL Trados Studio 2019 translation software https://www.youtube.com/watch?v=-QxkyjtP5zM Please read Revisiting Translation Quality Assurance (see attached) Google Translate adds 24 new languages, including its first indigenous languages of the Americas _ Article Assignment:, please response to the following questions: B (1) Please respond to the following questions in paragraph format (2 paragraphs minimum), providing examples when requested. All responses are to be in complete sentences. If you are referencing an article, journal, or external resource, please cite this reference. What is a CAT Tool and how does it support language services? Response of 150 words. What is a translation memory and what is its purpose? How do you create a translation memory on SDL Trados? Response of 150 words. What is Translation Quality Assurance? What do we look for when completing this process and what are the challenges faced? Response of 150 words. What are the translation technology standards and how are they determined? How does it vary from Localization Industry Standards Association (LISA)? Response of 150 words. Google introduced 24 new languages, including its first indigenous languages of the Americas. How does this support globalization and language sustainability? What does the article mean when referencing “supporting low resource languages with less training data”? Response of 150 words. B (2) Please respond to the following scenarios in paragraph format (2 paragraphs minimum), providing examples and problem-solving strategies . All responses are to be in complete sentences. If you are referencing an article, journal, or external resource, please cite this reference. SCENARIO 1: Response of 150 words Create a project estimate for the following request: Please translate this software manual (approx. 18,000 words) and giving you a 6 day turnaround time (TAT), including the weekend, for a total of a 4 business day TAT. On average, translators are able to translate 2,000 words per day. On average, editors and proofreaders can accommodate 3,500 – 5,000 words per day. The client is requesting that this translation following these steps – translation, editing, proofreading, QC. Translator costs 0.9 cents per word Editor costs 0.6 cents per word Proofreader costs $30 per hour (2,000-3,000 words per day) QC is completed internally but will cost the client min. $400. The project must make a 40-50% profit. Please create an estimate and list any challenges that may arise. SCENARIO 2: 1. Response of 200 words. A client you have already provided an estimate, deadline, and begun on the project is requesting additional services and for you to meet the same projected deadlines. The client is asking for Desk Top Publishing (DTP) and Accessibility QC. These additional services add a cost of $2,800, without including a rush fee. Please strategize and respond to the following questions. How will you respond to the client’s request? Will you be able to complete this task within the set deadline? If not, how will you adjust the timeline and update the client? How will you assure quality during the execution of a project? Student participation includes the following attributes: Submissions utilize correct word usage, spelling, and punctuation. All non-original work MUST be cited, in text and at the end of the document, in APA format, including both published and unpublished sources. To avoid plagiarism, other people’s words MUST be quoted and cited, and other people’s ideas must be cited. For an overview avoiding plagiarism, visit the Purdue University Online Writing Lab: https://owl.english.purdue.edu/owl/resource/589/01/. INSTRUCTIONS TO THE WRITER: Please read the required reading(s). See attached material(s) Follow the instructions above. Sources cited should be from reading documents. Thank you, Customer
TT-Assig #4 – see attached
CHAPTER 1 Introduction Translation and technology: disruptive entanglement of human and machine Minako O’Hagan Background This book builds on the increasing evidence of the impact of technology on contemporary translation, which serves diverse communicative situations across languages, cultures and modalities. The 2018 European Language Industry Association (ELIA) survey of over 1,200 respondents across 55 countries highlighted 2018 ‘as the year in which more than 50% of both the companies and the individual language professionals reported as using MT’ (ELIA 2018: n.p.). Although the ELIA report is cautious not to overstate the penetration of MT, concluding that the use of MT in the translation industry is not yet mainstream, it is clear that technology has already profoundly affected the way translation is produced. Similarly, the wider public is exposed to machine translated texts of varying quality in different scenarios, including user-generated content (e.g., social media postings) and information gisting for personal use (e.g., hotel reviews). Furthermore, portions of the increased production and circulation of translations are attributable to the work of fans, volunteers or activists who have different backgrounds and motivations, yet are operating in parallel to their professional counterparts. The increased visibility of non-professional translation (NPT) can be traced to the availability of technology-supported social and collaborative platforms, on which NPT typically operates (see Chapter 14 by Jiménez-Crespo). In this way, technology has contributed to translation of diverse types and quality, accompanied by an increasing awareness in society at large of translation and the role played by technologies in the translation process. More recently, the newest MT paradigm, neural MT (NMT) is making inroads into translation practice and adding to substantial research interests in Translation Studies (TS), as demonstrated in this volume. The influence of technology, ranging from translation-specific technologies such as MT to more general-purpose speech technologies and cloud computing, is far-reaching and calls into question some of the assumptions about who should translate, how and to what level of quality. Commercially viable translation today is all computer-aided (or -assisted) translation (CAT) and has been for some time. This is a term which comes across as somewhat redundant, given the ubiquitous use of computers in text production practices in general, except that the extent and the nature of the computer aid is constantly shifting. Another frequently used term in the translation industry is translation environment tools (TEnTs), which conveys an image of translators’ work surroundings being enveloped by technology. Among the newer terms coming into use is augmented translation (AT), introduced by Common Sense Advisory (Lommel 2018). AT puts the human translator in the centre (Kenny 2018), supported by an advanced suite of technologies, including automated content enrichment (ACE). This allows automatic searches of relevant information associated with the source content and informs the translator and MT to generate better translation (Lommel ibid.). AT and ACE concepts align with AI-supported medicine, which augments human expert judgement with rapid access to vast and relevant key information (see Susskind and Susskind 2015). Such complex technological infrastructure shaping macro and micro translation environments in turn relies on ongoing behind-the-scenes standardization work (see Chapters 2 and 3 by Wright and Roturier respectively) to ensure that all technological elements meet required standards and can therefore interoperate. However, the technology-driven modus operandi and technology-based infrastructure on which translation increasingly rests adds to quality concerns (see Pym in Chapter 26). For example, according to nearly 2,800 respondents to the SDL Translation Technology Insight Survey (SDL 2016), quality is currently of the utmost concern for the translation industry. These snapshots highlight that the human–machine relationship is in a state of flux, with uncharted paths ahead. While human translation shapes and is shaped by technologies, we do not know exactly how this process will unfold. This contributes to a sense of uncertainty among professional translators, which Vieira (2018), following Akst (2013), calls ‘automation anxiety’ (also see Kenny in Chapter 30). In the midst of ongoing technological transformation, this collected volume is not about translation technology per se. Rather, it is about understanding the dynamic relationship being formed between translation and technology from a range of perspectives. In doing so, it aims to increase our awareness of how contemporary translation is evolving and what it means to be a translator, as the co-existence of human and machine could be qualitatively different in the near future. Such a theme has become a major agenda of the 21st century across different types of work, particularly with AI beginning to affect areas previously considered only fit for humans (Susskind and Susskind 2015, also see Chapter 30 by Kenny). This volume attempts to tackle the topic both at a technical and a philosophical level, based on industry practice and academic research, to present a balanced perspective with TS contributions to a dialogue of global importance. Historical contexts of research on the nexus of human and machine in translation For translation, the explicit connection with ‘the machine’ started in earnest in the 1950s, with research and development (R&D) of MT as a new field for the non-numerical application of computers instigated by the Weaver memo (Weaver 1949) (see Melby in Chapter 25). However, as is well known, the 1966 Automatic Language Processing Advisory Committee (ALPAC) report put an abrupt end to MT R&D, especially in the US, for nearly a decade. Despite this, the frequent references to the ALPAC report in this volume and elsewhere are arguably evidence of its continuing legacy, which is perhaps not all short-sighted and misguided. For example, its support for ‘machine-aided translation’ has become mainstream in the translation industry under the banner of CAT. Martin Kay’s translator’s amanuensis (Kay 1980/1997) envisioned an incremental adaptive electronic aid for the human translator. Similarly, Alan K. Melby’s work on the translator’s workstation (Melby 1981) embodied a workbench integrating discrete levels of machine aid. Reviewing these pioneers’ concepts, Hutchins (1998: 11) highlighted how, in both cases, the human translator had been placed in control as someone who would use such tools in ways s/he ‘personally found most efficient’. The questioning of this centrality of human translators in today’s transforming translation workflow (Kenny 2018), further validates the aim of this volume to investigate the relationship between human and machine and its ramifications. Initially CAT tended to be distinguished from MT on the assumption that in the former, it is the human who translates (e.g., Bowker 2002, Somers 2003), whereas MT is automatic computer translation without human intervention. However, this division has become blurred as MT is increasingly integrated into CAT environments (see Kenny in Chapter 30) where the human translator is presented with translation proposals from (human produced) translation memory (TM) matches, together with MT outputs. Similarly, the increasing practice of post-editing of MT (PEMT) is reflected in a growing body of research which has rapidly reached a critical mass especially in translation process research (see collected volumes such as O’Brien 2014, Carl, Bangalore and Schaeffer 2016). There has been considerable progress made to address the earlier disconnect between MT research and research in TS, although the tendency to exclude professional human translators is still observable ‘in certain quarters of MT research’ (Kenny 2018: 439). Initially MT research focused on the application of computers to human language, with computer scientists and engineers ‘knowingly or unknowingly’ attempting to ‘simplify the translation process’ or ‘downplay the nuances of human language’ (Giammarresi and Lapalme 2016: 218). But the lack of cross-fertilization can also be blamed on the TS camp, with too few scholars interested in translation technology to widen the scope of translation theory, so that it could consider the increasing integration of technology into the translation process (O’Hagan 2013, Jakobsen and Misa-Lao 2017). In fact, the connection between translation research and MT research can be traced to the 1960s when the idea of equivalence relationships between source and target texts was explored by linguists such as Catford (1965). In particular, Catford’s idea of a translation rule as ‘an extrapolation of the probability values of textual translation equivalents’ (1965: 31) is of direct relevance to subsequent data-driven approaches to MT (Kenny forthcoming), which are based on the use of parallel texts (or bi-texts) (see Simard in Chapter 5). In the 1960s, when Chomsky’s linguistic theory (Generative Grammar) was exerting its influence, including on MT, Eugene Nida was among the few early translation theorists cognizant of MT research, and related to it in his foundation work Toward a Science of Translating (Nida 1964). In his endeavour to bring theorizing about translation into the scientific arena, Nida applied Chomskian linguistics and the information theory approach to communication (Nida 1964, Nida and Taber 1969). It is relevant to recall the fact that MT R&D precede the development of TS; it was only in 1972 that James Holmes (1972/1988) named the discipline as ‘Translation Studies’ (abbreviated as TS in this article) and laid the foundations for theorizing translation to ‘explain and predict’ translation with ‘description’ as the first step. In the 1980s TS was shifting away from a linguistic focus to a consideration of broader contexts through functionalism. Attention moved from the source to the target text and translation as action, before the cultural turn in the 1990s moved human translation largely outside the scope of interest of MT circles. Into the 1990s and 2000s technologies played a key role in empirical TS research by providing research tools, including some for corpus analysis. Other tools, such as keyboard logging (e.g., Translog originally developed by Arnt Jakobsen at the Copenhagen Business School in the late 1990s) and eye tracking (see Jakobsen in Chapter 24), were also introduced more widely into TS, and these have been used to better understand translator behaviours and the behaviours of translation users in the context of translation reception; for example, in audiovisual translation (AVT) (see Kruger 2018). In particular, these research tools contributed to the further development of cognitive translation studies as a specialized field of research (see Schwieter and Ferreira 2017), one which is now set to probe neural representation with non-invasive neuroimaging techniques, such as functional magnetic resonance imaging (fMRI) and positron emission tomography (PET) (see Shreve and Diamond 2016: 155). This brief look back at the trajectory of the connection between translation and technology shows increasing ‘border crossings’ (Gambier and van Doorslaer 2016) to neighbouring disciplines such as computer science, computational linguistics and now neuroscience. Aim and scope of the publication The spread of computers across global markets gave rise to new areas of practice and research in TS, such as localization (see Folaron in Chapter 12). This saw TS scholars engaging more fully in theorizing about technologies by tapping into sociological, cultural or philosophical aspects (see Chapters 23 and 31 by Olohan and Cronin respectively), on the one hand, and cognitive or usability/ergonomic dimensions on the other (see Chapters 21 and 24 by Ehrensberger-Dow and Murphy; and Jakobsen respectively). There is also a large body of knowledge being accumulated in translator training and education focused on technology (see Kenny in Chapter 30). Furthermore, as a result of technological advances, research-led practices are becoming more common in fields such as accessibility and universal design (see Remael and Reviers in Chapter 29). In this way, technology more than anything else started to bring together the interests of academy and industry. Technological dimensions continue to present fresh scope to bridge the gap between translation theory and practice, ideally to respond to ever-present translator suspicions as to the usefulness of theory in actual translation practice – a topic earlier addressed in Chesterman and Wagner (2002) and more recently in Polizzotti (2018). As demonstrated in this volume, the exploration of the relationship between technology and translation is leading to a fresh examination of contemporary translation benefitting not only translators as users of technologies but also those who develop and research translation technology. It is hoped that this volume contributes critical insight into the complex symbiosis between humans and machines so that translation (and interpreting, which is covered to a limited extent in this volume) can serve increasingly diverse communication needs in the best and most sustainable way. With the above overall goal of the publication, the Handbook has a number of specific features. First, it is designed to represent the interests of different stakeholders in the translation industry. The fragmented nature of the translation industry is recognized as it affects the level of implementation and the types of technologies used in translation. The translation industry consists of a large population of freelance translators (see Zetzsche in Chapter 10) and language service providers (LSPs) which range from small-and-medium-sized (see King in Chapter 9) to multinational vendors (see Esselink in Chapter 7). In addition, often well-resourced public international organizations (see Caffrey and Valentini in Chapter 8) play an important role as early adopters of new technologies. Although not officially part of the industry, non-professional translation is also contributing to translation production, forming part of a participatory culture (Chapters 13 and 14 by Altice and Jiménez-Crespo, respectively). Similarly, the use of translation technology in (second) language learning is part of the picture in the technology and translation alliance (see Chapter 11 by Yamada). The volume therefore reflects different settings for technology uses according to the different segments of the industry as users of translation technology, encompassing contributors who reside outside academia. Secondly, this publication attempts to make sense of the current position of technology from diachronic perspectives. What is considered new technology often had a prior incarnation as a rudimentary prototype or an embryonic concept which needed further maturing, perhaps requiring relevant surrounding technologies and conditions. While historical approaches are well explored in TS research in general, their application in the context of translation technology research has not been traversed to the same extent. In the context of MT, John Hutchins was the first to demonstrate the merit of a historical approach with his comprehensively chronicled Machine Translation: past, present, future (Hutchins 1986). The Routledge Encyclopedia of Translation Technology (Chan 2015) is a more recent example also with regional foci. Among the many chapters in the present volume which provide a historical trajectory, historical perspectives are more applicable and prominent in certain chapters. For example, Sue-Ellen Wright in her chapter on Standards follows periodization, drawing on Galinski (2004 cited in Wright) to cast a spotlight on key phases of the evolution of approaches and applications of standardization across language, translation and the localization industry. Similarly, Debbie Folaron (Chapter 12), in discussing technical translation as an established practice and localization as a relatively new addition within TS, traces their historical trajectories. The historical approach contexualizes and recontextualizes the development of specialized translation practices in dynamic interaction with technology. Such an approach allows Folaron to present a critical discourse on the links between technology and localization as well as technical translation, enabling the author to systematize the epistemology of the field. In turn, Sabine Braun (see Chapter 16 on technology and interpreting) tracks technological developments in telecommunications which have shaped varied modes of distance interpreting and configurations of technical settings. This richly traces the new demands on professional interpreters to serve different technological constructs as their working environments. Thirdly, this volume addresses a number of substantive matters under Part V as overarching issues that challenge translation practice and research concerned with technology, ranging from quality to ecology. This part, along with the research foci and methodologies addressed in Part IV, aims to provide scholars and industry players with key topics, future avenues for research and analysis and insight into the implications of technologies for translation. Finally, the volume takes into account readers who may not be familiar with the topics addressed by some chapters and provides additional information: a list of relevant standards in Chapter 2, a glossary of terms in game localization in Chapter 13, an explanation of eye tracking technology in Chapter 24 and a list of recent major funded projects relevant to accessibility research in Chapter 29. In terms of the macro-structure, Part I addresses key underlying frameworks and related technologies as relevant across different modes and areas of translation. Part II examines the adoption of technologies by different user groups. Part III considers the impact of technologies on each of the distinctive areas of translation (and interpretation) practice. Part IV discusses research settings and methodological issues for selected research areas particularly relevant to the emerging relationships with technology. Part V explores the overarching issues in TS resulting from the increasing influence of technologies. The micro-structure of each chapter has certain key elements that are common across all chapters, yet is not uniform, as the final decision on the key content was left to the liberty of the chapter authors. The cross-referencing to other chapters was mostly added by the editor. The next section provides an overview of how each contributor addresses their specific topic. Part I: Translation and technology: defining underlying technologies – present and future Part I consists of five chapters which explain the fundamental building blocks and related general-purpose technologies key to understanding translation and technology at present and in their emerging guises. In Chapter 2, ‘Standards for the language, translation and localization industry’, Sue Ellen Wright provides a historical overview of how and why standards have developed over time, concerning technology applications in sectors spanning the translation, language and localization industry. Various standards for processes, products and services in today’s complex technological world play a key role, including generating the basis for a ‘feedback-rich information life cycle’ beyond individual documents which may be chunked, repurposed and retrieved. Drawing on Briggs (2004 cited in Wright), Wright stresses, ‘[s]‌tandards transform inventions into commercial markets’. This is why international cooperation and expert consensus in a given field are critical in setting standards. Wright uses a historical approach to illustrate the role of standards and connections among them, without which today’s technologically interlinked world through the Internet and use of tools in collaborative modes would not have been possible. A closely linked theme is taken up in Chapter 3, ‘XML for translation technology’, by Johann Roturier. Roturier shows how XML forms key backbone file exchange standards to ensure interoperability between translation tools and also the offline portability of tools in different user settings. The significance of XML can be illustrated in the statement, as quoted by Roturier, that ‘over 90% of data for translation is generated with XML’ (Zydroń 2014 cited in Roturier). Nevertheless, as the chapter explains, dynamic changes including the emergence of non-proprietary open source formats mean that this is a constantly developing area. Translators working especially in areas such as localization face the issues associated with these underlying factors in dealing with translation tools and files. Chapter 4, ‘Terminology extraction and management’, by Kyo Kageura and Elizabeth Marshman addresses terminology extraction and management as particularly pertinent in specialized translation (see Chapter 8 by Caffrey and Valentini). Terminology was one of the earliest areas within the translation workflow to have exploited electronic, as opposed to manual, processing, yet efficient terminology management within real-life translation practice remains a challenge. The chapter explains in some detail the different methods used in automatic term extraction (ATE), which is a critical upstream process, but is a computationally complex task to perform. The authors see ATE as a challenge especially in terms of quality, as is the case with collaborative terminology management. Finally, the role of terminology in connection with data-driven MT, including NMT, is briefly discussed, highlighting the importance of terminology quality in the training data. Here the need for human judgement at critical junctures within terminology management is stressed. Related to the theme of electronic processing of linguistic resources, the following chapter focuses on linguistic data as a bi-product of, and an ingredient for, translation technology. In Chapter 5, ‘Building and using parallel text for translation’, Michel Simard explains the key techniques behind collection, structure, alignment and management involved in parallel text (consisting of an aligned source text and target text pair). These issues gained great importance with the widespread adoption of TM and data-driven MT, which use parallel text as training data. In reference to the more recent word alignment process in NMT, Simard refers to a ‘soft’ alignment mechanism known as ‘attention’. The anthropomorphic use of ‘attention’ in reference to a computational operation highlights its human-like function, albeit one not always achieved successfully. In turn, the lack of trust by human translators towards MT outputs, as alluded to by Simard, aligns with the findings elsewhere in TS literature (see Chapter 19 by Vieira). The last point signals some fundamental questions that arise when thinking about human–machine cooperation in translation. Further probing the cooperative dimension, the next chapter turns the focus to general-purpose technologies whose relevance to translation is increasing. In Chapter 6, ‘Speech recognition and synthesis technologies in the translation workflow’, Dragoș Ciobanu and Alina Secară examine the development and deployment of speech technologies i.e. speech-to-text and text-to-speech and their emerging uses in the translation workflow. While the authors find actual use cases of speech technologies in CAT scenarios are currently limited they point to the way in which speech recognition systems are integrated into live subtitling in ‘respeaking’ mode (also see Remael and Reviers in Chapter 29). The chapter reports recent empirical research conducted to test productivity gains and quality issues when combining automatic speech recognition systems in the process of translating as well as other tasks, such as revision and PEMT. The results highlight productivity gains as well as accuracy and stylistic issues while also pointing to the need for improvement in achieving a smoother integration of such technologies into CAT tools, together with consideration of task types. Part II: Translation and technology: users’ perspectives Consisting of five chapters, this section addresses the perspectives of different translation technology users. The chapters represent different sectors of the translation industry. It ranges from large-scale language service providers (LSPs) and public institutions to freelance translators as well as language learners and translation practitioners who are not professional translators, but who benefit from translation technologies. Chapter 7, ‘Multinational language service provider as user’ by Bert Esselink looks into large LSPs for their use of technologies centred on translation management systems (TMS) which are divided into: Process Management and Automation, Project Management and Administration, Customer Management and Commerce, and Translation and Quality Management. The detailed description of the features and functionalities of TMS gives insight into how technologies are used to deliver an optimum translation service to customers by large LSPs. The chapter signals the increasing presence of AI and its likely significant impact in future, including in the area of project management, with implications for substantial change to the current human-based model. In Chapter 8, ‘Application of technology in the Patent Cooperation Treaty (PCT) Translation Division of the World Intellectual Property Organization (WIPO)’ Colm Caffrey and Cristina Valentini provide the perspective of a large public institution as a technology user. Patents form one of the most targeted fields of specialized translation heavily facilitated by technology. Caffrey and Valentini describe how TM and terminology management systems are used in the PCT Translation Division, with its concerted efforts to provide translators with sophisticated terminological support via their terminology portal WIPO Pearl. Such terminological resources are a result of the integration of corpora, MT and machine learning algorithms, which may not be achievable by smaller organizations, let alone freelance translators. The authors further report on WIPO NMT which has been used since 2017 for all of the Division’s nine languages, benefiting from a large body of in-domain training data (i.e. parallel corpora) available in-house. However, the authors suggest that the integration of NMT into the workflow means a change in the way translators deal with particular characteristics of NMT output which may be fluent yet contain terminological issues. This in turn implies different ways of using the terminological resources independently according to the need of the translator. Compared to large organizations, smaller translation operators have different settings and contexts in which to consider technologies, as described by Patrick King in Chapter 9, ‘Small and medium-sized enterprise translation service provider as technology user: translation in New Zealand’. Drawing on his experience as a translator, editor and translation company operator, King explains how a medium-sized LSP in New Zealand is implementing technologies to achieve a productivity gain while maintaining translation quality. In particular, he shares translators’ perspectives on new technologies, showing evidence of the openness of (some) translators to using technology, and that of NMT in particular. At the same time, King advises that technology should be assessed ‘on its own merit’, not simply because it introduces some improvements on the previous version. These days, most LSPs and freelance translators alike operate internationally, yet local contexts are still significant, as in New Zealand where Māori and South Pacific languages have unique requirements. King reminds the reader of the reality of translation service operating requirements, for example, dealing with a range of languages with unequal levels of compatibility with machine-processing. The fragmented translation industry continues to be supported by a large number of freelance translators. In Chapter 10, ‘Freelance translators’ perspectives’ Jost Zetzsche opens the discussion by defining what a freelancer is and then moves on to examine key issues which initially delayed the uptake of technologies by freelance technical translators. By tracing a historical trajectory since the 1990s when CAT tools first became widely available, Zetzsche shows why uptake was initially relatively low and how translators changed from careful crafters of text to recycling ‘CAT operators’ who ‘fill-in-the-blanks’. He argues that, at least in certain contexts, some tools are found to be ‘stifling instruments for the human sensitivities of the technical translator’. Among the high use general-purpose technologies, Zetzsche highlights freelance translators’ use of social media platforms from relatively early on, such as various online translator forums as a means to stay in contact with peers rather than for finding clients. The author points out that freelance translators tend to see the value of technology investment for its immediate link to increased revenue and this is why terminology management is a constantly undervalued element. He observes that MT is more accepted by translators compared to CAT when it was first introduced. Into the future with the increasing use of AI, Zetzsche sees the ideal role of translators as providing support by guiding technology developers. Chapter 11, ‘Language learners and non-professional translators as users’ by Masaru Yamada shifts the focus from the role of technology in official translation service provision to that of second language learning. Yamada explores the link between translation technologies and TILT (Translation in Language Teaching), with language learners and also non-professional translators using such technologies to improve their second language competency. Based on recent research on TILT, the chapter highlights the benefit of using MT output as a ‘bad model’ to boost language learners’ competency through post-editing (PE) tasks. Furthermore, Yamada draws on research pointing to the benefit of human-like errors made by NMT, which incur a higher cognitive effort in PE compared to errors produced by SMT, which are generally easier (more obvious) to repair. The former are therefore more conducive to learning. The capacity of translation technologies to boost lesser skilled translators’ abilities is seen as empowering in this chapter. Yamada suggests the use of translation technologies in TILT could logically link to Computer-aided Language Learning (CALL), providing further research avenues. Part III: Translation and technology: application in a specific context – shaping practice The technologization of translation is affecting different translation practices but with specific implications for each specialized area. Part III looks into different translation practices divided into eight chapters. In Chapter 12, ‘Technology, technical translation and localization’, Debbie Folaron takes on technical translation and localization to deconstruct their relationship with technology, taking a historical, methodological and critical approach. Through such lenses the chapter highlights, for example, how the emergence of localization practice has cast this new practice in relation to globalization, as articulated in the industry framework of Globalization, Internationalization, Localization and Translation (GILT). Furthermore, the localization process, which cannot be completed without the use of a technological platform, led to the development of specialized tools, in turn contributing to the formation of localization ecosystem (also see Cronin in Chapter 31). Folaron demonstrates the relevance of a critical digital discourse in shedding light on such practices as localization which is intertwined with digital artefacts. She then calls for TS scholars to engage more with the field of digital studies, which provides scope for the critical analysis of translation practice in an increasingly digital world. In Chapter 13, ‘Technology and game localization: translation behind the screens’ Nathan Altice inadvertently responds to Folaron’s call to engage with digital studies with his discussion on localization of video games, especially by fans as non-professional localizers. Focused on the technicity of game hardware and software, Altice identifies a core feature of game localization with the practice of ROM (Read Only Memory) hacking, which involves unauthorized access and modification of a game’s ROM by game fans, including the modification of the original language of the product. Characterized by its subversive and highly technical nature, ROM hacking communities continue to be active and visible. Informed by platform studies perspectives within game studies, Altice shows how ‘language’ is encoded ‘graphically, materially and procedurally’ by design in both the console/platform (hardware) and the game (software). This topic then naturally links to the following chapter focused on the broader concept of non-professional translation (NPT), which has recently gained considerable research interest in TS. In Chapter 14, ‘Technology and non-professional translation (NPT)’ Miguel A. Jiménez-Crespo examines the phenomenon of NPT, exploring its symbiotic relationship with broader technological developments represented by Web 2.0. The chapter gives close scrutiny to the increasingly visible practices of NPT, such as translation crowdsourcing and online collaborative translation. NPT involves participants who are not ‘classically’ trained translators, operating as part of translation communities in diverse contexts from pursuing fandom to activism or humanitarian initiatives. The chapter highlights the close correlation between NPT and digital technologies. NPT is characterized by non-uniform uses of translation technologies compared to its professional counterpart. Consequently, human–machine interaction in NPT can often be different from that in professional translation, adding to the complexity of such relationships in contemporary translation. NPT encroaches on a variety of research foci, ranging from audiovisual translation (AVT) to PEMT, as well as raising questions of quality and ethics, affording scholars multiple lenses of analysis. Within TS literature, localization and AVT are considered to be the areas most affected by new technologies and as a result having the greatest influence on the theorization of translation (Munday 2016: 275). In Chapter 15, ‘Technological advances in audiovisual translation’ Jorge Díaz Cintas and Serenella Massidda reflect on some of the formidable transformations within the rapidly expanding field of AVT. The chapter surveys an increasing body of research on the application of TM and MT in AVT, although the authors point out the benefit of these technologies is currently relatively limited. Cloud subtitling is seen as a new way for professional translators from different geographical locations to work together on collaborative platforms. Cloud-based dubbing and voiceover as end-to-end managed services are shown as rapidly developing examples. The authors explain how the availability of a wide range of tools and platforms is having a democratizing impact on AVT, yet is also heating up the competition among industry participants and causing increased anxiety among professional translators. The authors observe the way technology is altering relationships between stakeholders, highlighting its deep-seated impact. Translation technologies are seen to be closely associated with (written) translation, yet MT is also core to machine interpreting (MI) which combines MT with speech technologies. In Chapter 16, ‘Technology and interpreting’, Sabine Braun focuses on the field of interpreting, including the rising demand for ‘distance interpreting’ and the milestones in MI. The chapter provides a comprehensive survey of the historical development of technologies shaping distance and on-site computer-assisted interpreting by humans, introducing different terminology used for different technology application settings and configurations of participant locations. While MI currently cannot service situations requiring highly accurate professional interpreting, Braun suggests that ongoing research, especially into neural networks, provides scope for further development. Highlighting the increasing reports by remote interpreters of psychological and physiological problems, the author stresses that interpreting is a cognitively challenging task and any other distracting issues relating to the lack of physical presence can affect the interpreter’s performance. At the same time Braun raises the question of the sustainability of the profession as an important consideration in light of implementing smart technologies. Overlapping with some of these concerns, in Chapter 17 ‘Technology and sign language interpreting’, Peter Llewellyn-Jones addresses settings specifically for Deaf people. Beginning with how the invention of the telephone disadvantaged the Deaf community, the author charts the development of spoken-signed language interpreting services via telephone, computer and video links. Comparing the situation in the US to Europe, the UK and Australia, the chapter argues that services such as Video Relay Services (VRS), where all interlocuters are in different locations, or Video Remote Interpreting (VRI), where only the interpreter is in a separate location, should not be developed simply to exploit available technologies; they must be carefully thought through to adequately enable the highly complex cognitive task of sign interpreting. Drawing on the research literature, Llewellyn-Jones illuminates the serious consequences that can result from making decisions purely based on the cost efficiency seen to be achieved by the use of technologies. As touched on in the earlier chapter by Jiménez-Crespo, technologies are increasingly used to facilitate volunteer translators’ involvement in humanitarian causes. A tragic reminder of the need for ‘crisis translation’ is the 2017 Grenfell Tower fire, in which the apartment block’s multi-ethnic occupants speaking a diverse range of languages were largely unable to receive accurate information in their language in a timely manner. In Chapter 18, ‘Translation technology and disaster management’, Sharon O’Brien homes in on the role of technologies in disaster management and translation, which constitutes a relatively new area of research in TS and elsewhere. O’Brien argues translation is a neglected aspect in disaster management literature and policy, yet its role can be significant. This chapter illustrates the function of translation with the use of technologies serving beyond the ‘response’ phase of disaster risk management to all of the ‘4Rs’: the pre-disaster phases of ‘reduction’ and ‘readiness’ and the stages of ‘response’ and ‘recovery’. However, despite the proven successes with translation technologies in disasters such as in the Haiti Earthquake, technology deployment can be challenging, given issues such as disrupted infrastructure. Additionally, information recipients of written texts may have differing levels of literacy, not to mention cultural and accessibility considerations. Above all, this field highlights the socially significant role of translation, with challenges ahead including ethical considerations, linking to translation ecology thinking (see Cronin in Chapter 31). During the second decade of the new millennium, the use of MT within professional translation has become highly visible, with a raised interest in post-editing, as discussed at the beginning of this introduction and also amply demonstrated by the contributors to this volume. In Chapter 19, ‘Post-editing of machine translation’, Lucas Nunes Vieira gives a comprehensive survey of the growing research interest and industry practice of post-editing of Machine Translation (PEMT). Vieira begins the chapter by reminding us that PE used to be a ‘machine-centric’ activity in a mode of ‘human-assisted machine translation’ but is now geared rather towards ‘machine-assisted human translation’ in CAT environments. Drawing on the literature, Vieira presents the evolution of post-editing as a spectrum from MT-centred (automatic PE) to human-centred PE (interactive/adaptive) (also see Chapter 22 by Läubli and Green). Vieira sees the future of PE as better integrated into the professional translation process, where PE is no longer a discrete task. His conclusion highlights the need for further research into human agency in relation to PE activities and wider CAT environments. Vieira then highlights the role of TS in providing evidence-based findings to temper the hyperbolic claims made by some NMT developers and enables well-informed assessments to be made about technology. Part IV: Translation and technology: research foci and methodologies This section consists of five chapters which address specific foci and methodologies adopted to answer research questions probing the relationship between translation and technology. In Chapter 20, ‘Translation technology evaluation research’, Stephen Doherty highlights how translation technology evaluation has gained key importance due to the prevalent use of technologies in contemporary translation. In particular, MT and post-editing have provided a strong impetus for this research area with the continuing development of automatic evaluation methods (AEMs) to complement or as an alternative to human-oriented evaluation of MT. Technology evaluation affects different stakeholders who have diverse needs, including technology developers and suppliers as well as providers and buyers of translation products and services, end-users and translation researchers. Doherty argues that despite the often-highlighted differences in purpose and context between evaluation methods used in academia versus in industry settings, the evaluation process is inherently the same in that the evaluator needs to align the evaluation purpose with the available resources and methods, and the desired format. While advances in technology evaluation research are providing increasingly sophisticated evaluation mechanisms, Doherty calls for further research focused on three areas: universalism and standardization, methodological limitations and education and training. These will allow more inclusive and standardized approaches to meet the needs of the different stakeholders. In Chapter 21, ‘Translation workplace-based research’ Maureen Ehrensberger-Dow and Gary Massey provide an up-to-date survey of workplace-based research, which has steadily gained importance in TS over the last decade. This is where research moves out of the translation classroom or laboratory into real life workplaces, filling the gap in the other research settings and providing ecological validity by capturing data from translators in situ. Ehrensberger-Dow and Massey show how increasing technologization has made it relevant to see expert activity as a manifestation of situated cognition, whereby human cognition is assumed to extend beyond individual minds to, for example, interaction with technological artefacts. The chapter articulates the way workplace-based research can highlight with empirical data how technologies can facilitate or disrupt the human translation process. The chapter calls for more transdisciplinary action research to ensure human translators are empowered by working with technologies and not undermined by their technological environments. In Chapter 22, ‘Translation technology research and human-computer interaction (HCI)’ Samuel Läubli and Spence Green address translation and technology from the perspective of professional translation as HCI. Focused on users of translation technology, they discuss ‘interactive MT’ (IMT) as opposed to the ‘static’ model (also see Vieira in Chapter 19), and examine factors behind the often-negative response of professional translators to PEMT tasks. The chapter draws on empirical evidence to highlight how seemingly trivial User Interface (UI) design issues, such as font size, lack of shortcuts, copy–paste functionality, etc. can hinder efficient human-computer interaction. Similarly, the authors point to the findings in the literature that user irritation relates, above all, to the repeated need to correct the same MT errors. The authors surmise the key challenge in HCI as the limitation of the machinery’s ability to learn from (human) users, whereas humans can learn to use ‘novel machinery’. Furthermore, ‘making the state and effects of adaptation understandable to their users’ is part of the challenge in creating adaptive systems. This in turn critically requires the iterative involvement of translators in the development process, a lesson still being learnt from the early MT projects that lacked translator participation. In Chapter 23, ‘Sociological approaches to translation technology’ Maeve Olohan examines the key research questions and methodologies in sociologically-oriented studies on translation technologies. The chapter traces the development of SCOT (social construction of technology) as a field of study to demonstrate how ‘science and technology are socially constructed cultures’ (Pinch and Bijker 1984: 404 cited in Olohan’s chapter), accommodating both successful and failed technologies. In parallel with SCOT the author explains other sociological approaches applied in TS research. Despite the increasing use of sociological approaches in TS research to shed light on translation technologies, Olohan concludes that there is more to pursue in ‘sociology of translation’, both conceptually and empirically. For example, she argues that critical theory of technology can be fruitfully combined with constructivist approaches to reveal unequal power distributions, which often affect the adoption of technologies. Olohan suggests these lines of inquiry could lead to a further renewal of the traditional conceptualization of translation. Methodological innovations are part of the increasing sophistication of research in TS and eye tracking is one of the key examples. In Chapter 24, ‘Translation technology research with eye tracking’, Arnt Lykke Jakobsen provides explanations about eye tracking technology and a detailed survey of this popular research tool now used in diverse areas of TS. This chapter shows how eye tracking software can trace with fine granularity the translation process and the dynamics of the translator’s interaction with translation technology, for example TM or MT, while performing translation or post-editing. Or it can capture the translation user’s response to dynamic text presentation modes, such as in subtitles. Translation is an ‘effortful cognitive activity’, yet to what extent technological tools add to or lessen such efforts is a question which calls for empirical evidence. Jakobsen suggests eye tracking could provide insight, for example, into reasons for ‘the global preference of multimodal, analogic communication’ compared to ‘unimodal, symbolic communication’ despite the assumption that the former is more effortful. While cautioning that not everything about visual attention and cognitive processing is fully explainable from eye tracking data, Jakobsen predicts there are likely to be widening avenues for eye tracking in future as part of mixed-methods research design used with ‘qualitative data and neuroscience technologies’. Part V: Overarching issues The final section consists of seven chapters which focus on a number of critical overarching issues arising from significant uses of technology in translation. This section covers MT, quality, fit-for-purpose translation, accessibility, reuse of translation data, translator training and translation ecology. In Chapter 25, ‘Future of machine translation: musings on Weaver’s memo’, Alan K. Melby explores where the next paradigm of MT is headed, centring on the challenge arising from sub-symbolic deep learning (i.e. its inner workings are non-inspectable to humans) applied in the current generation of NMT. This issue of increased opacity in machine learning is noted by scholars as a cause for concern (see Kenny 2018). As a way to think about future developments of MT, Melby uses a detailed analysis of Warren Weaver’s 1949 Memorandum. The chapter treats the early pioneer’s concepts presented in the memo as ‘seeds’ behind the subsequent successive paradigms of MT, from RBMT (Rule-based MT) to SMT (Statistical MT) to the current evolving state of NMT. Melby then considers developments in the intervening times of enabling technologies and surrounding contexts to build his conjecture by mapping the seeds in Weaver to the MT paradigms. With sub-symbolic deep learning, Melby argues, even those who are modelling AI cannot seem to predict exactly what goes on inside the ‘black box’. The discussion leads Melby to the question of what it means to ‘understand’ the text in human and machine translation, which he believes is significant for the next phase of MT, i.e. seeing Weaver’s final seed – linking MT to the human brain – sprouting. Not unrelated to the issue of ‘understanding’, quality is a key challenge for translators and the translation industry. Pym clarifies the two meanings of ‘quality’, the first being ‘properties’ a la Aristotle, if used in the plural, and, in the singular, meaning ‘the relative excellence of the thing’ for a given purpose. The two meanings are often related, as we are reminded by Pym, with changes of properties leading to changes in the status of excellence, as applicable to the quality of translation technologies. In Chapter 26, ‘Quality’, Anthony Pym addresses translation quality in the context of translation technologies by treating it as ‘relations’ based on Chesterman (2004 cited in his chapter); namely relations between the translation and the target text, comparative texts, purpose, industrial standards and the translator. For example, with the prevalence of TM and MT in the translation process, Pym highlights the critical need for greater quality control of such technologies which are exerting ‘unspoken forces behind the industrial standards’. In reference to the relation between translation and the translator, Pym argues that a likely consequence of technologization manifesting in more pre- and post-editing for translators, could still be made satisfying for them, if such work was presented as ‘authorizing’ the final translation. He suggests it is up to translators and their employers to ensure that the work is recognized and rewarded as such. Discussing these relations, the chapter teases out human elements in quality to remind the reader that evaluations of quality ‘reside on human values’ that are ‘built on a fundamental indeterminacy’. As highlighted in his conclusion, Pym draws our attention to ‘the human in the machine’, so the quality debate is not overshadowed by the technology and the extreme ideological stances both for and against it. This chapter is followed by the closely related topic of ‘Fit-for-purpose translation’ in Chapter 27, where the indeterminate nature of quality is explored. Here Lynne Bowker discusses translation as a balancing act between the ‘triple constraint’ used in the project management field of quality, cost and time. Furthermore, the author points to ‘a perception problem’ in reference to the negative associations of the use of translation tools. Bowker reminds the reader that ‘translations can be commissioned for a diverse range of purposes’, while a translator’s job is to ‘choose the strategy best suited to producing a target text that fits the specified purpose’. With the technologization of translation, Bowker highlights, translators need to be able to optimize technologies to best meet different translation purposes, as specified by the clients. This may result in different levels of quality in translation, in conflict with professional ethics, which currently do not provide adequate guidance to translators in respect of the use of technologies. As much as there is a need for client education, Bowker stresses the need for professional translator (re)education to recognize the challenges and not denigrate translators who are catering for ‘bulk translation services’. The final thought offered by Bowker is indeed ironic as she suggests: if lesser quality translations produced for different purposes start to affect the quality of the training data for MT, in turn affecting MT quality, fit-for-purpose translation may inadvertently ensure the survival of human translators. Bowker’s last point relates to the increasing harvesting of translation as data used for machine learning, as discussed next. In Chapter 28, ‘Copyright and the re-use of translation as data’, Joss Moorkens and Dave Lewis address the increasing secondary use of translation currently treated as a cheap commodity. This is becoming an issue in the advent of data-driven MT and especially for NMT, due to its requirement for a significant amount of training data for machine learning. The authors highlight that the metaphor of ‘oil’ or ‘gold’ used for the translation as training data implies they are naturally occurring, which is untrue, giving rise to the question of translation rights. The issue is that this subsequent benefit generated by the original translation is not passed on to the translator who translated the text. In view of the 1889 Berne Convention, which codified the copyright of translation as a derivative work, the authors point out that the current reuse of translation as data was not envisaged in the Convention, nor was its potential liability in relation to NMT. They argue that current copyright laws are not equipped to deal with the situation of the reuse of translation data, while new proposals, such as digital commons with a range of rights, could potentially be applied through professional translation organizations. The authors suggest the latter is more conducive to ensuring the sustainability of the translation industry by improving the redistribution of equity within translation production networks. The authors suggest that this could be realized in collective agreements accruing royalties to the translators, as is already the case among some subtitlers in Nordic countries. However, the chapter concludes that the forthcoming EU Directive on Copyright in the Digital Single Market is not likely to resolve the issue of translation copyright, which will remain as a key question requiring the attention of translators. In Chapter 29, ‘Media accessibility and accessible design’, Aline Remael and Nina Reviers discuss media accessibility (MA), which has rapidly become integrated into research agendas in TS with practical implications for audiovisual translation (AVT) driven by digitization and globalization. The authors argue that in today’s ‘highly mediatized society’, the accessibility of audiovisual content, and eventually accessible design, has become a central concern for society at large. They assert technology is making it ‘theoretically’ possible to cater for all types of media users, given the right policy and legislation. MA involves ‘translation’ of an ‘intersemiotic and multi-modal’ kind where aurally or visually conveyed information is converted into modes to suit the target audience’s needs. For example, subtitles for the Deaf and the hard-of-hearing (SDH) involve a conversion from aural to visual mode where the target audiences can ‘read’ the dialogue. SDH now includes live subtitling, which is commonly delivered in the form of respeaking, whereby subtitles are generated synchronously through the use of speech recognition. The technology applications in this field are wide-ranging, from speech recognition and synthesis to MT as well as avatars used for sign interpreting. Initiatives on universal design for MA are well underway, with examples such as accessible filmmaking in which accessibility is foregrounded in the filmmaking process itself (Romero-Fresco 2018). Applying an actor-network theory framework, this chapter critically traces the developments taking place in media accessibility in which practice and research interact with technologies exerting considerable force as enablers. In the unpredictable technological milieu, the authors see ‘translation’ in its broad sense as playing a major role of a key ‘actant’ to progress this significant social issue of the modern age of technologies towards universal design. In Chapter 30, ‘technology and translator training’, Dorothy Kenny addresses the issue of translator training in the advent of technologization, comprehensively drawing on the growing literature in the field. Kenny argues ‘a nuanced understanding of how technology and translation are intertwined should be a vital ingredient of any broad education in translation studies’. Kenny therefore advocates the view that technological competence need not remain merely ‘instrumental’ but can make ‘a significant contribution to the development of critical citizenship’. The chapter provides a critical analysis of contemporary thinking behind translator training and education, which is then extended to a key concern for the long-term health of the translation industry, including economic factors such as ‘technological unemployment’ in the advent of AI. In the author’s words the next challenge lies in ‘the integration of machine learning into translator training’, which would signify indeed a paradigm shift in translator education. Implicit in this chapter is ecological thinking, viewing translation and technology as an intrinsic part of the technologizing global world, which relates to the theme of the next final chapter. In Chapter 31, ‘Translation, technology and climate change’, Michael Cronin interprets the agenda of translation and technology in terms of the big picture, employing ecological perspectives and proposing a new methodological approach based on eco-translation thinking. Cronin maintains that the fate of translation enterprise is inevitably implicated in what happens to technology which is, in turn, linked to accelerated climate change. This chapter constructs an argument through the notion of translation ecology, with the key concept of the ‘posthuman’ which provides an approach for understanding the deepening relationship developing between humans and digital technologies. Cronin insists on treating technology not as ‘an inert tool’ but as ‘an animated part of the human ecosystem, a constituent element of the translator’s transversal subjectivity’. His ecological thinking in turn gives rise to a renewed perspective on ethical issues, as Cronin asks: ‘Is it…ethically responsible and professionally adequate to train translators using technologies that will not be sustainable in an environmentally compromised future?’ This line of concern relates to crisis translation settings (O’Brien in Chapter 18), which may only allow low-tech solutions due to the destruction of the communications infrastructure. Also, it relates to the issue raised by Moorkens and Lewis (Chapter 28) in questioning the continuing secondary use of translation as if it is a bottomless resource to feed into MT until it is depleted – or until eventually the translation quality deteriorates as a consequence of fit-for-purpose translation (Bowker in Chapter 27). In this critical age of climate change and rapid technologization, Cronin directs our attention to planetary contexts as a productive way to locate translation through an eco-translation framework, as we grapple with the role of humans in relation to the role of technologies in translation research and practice. Joining Ehrensberger-Dow and Massey (Chapter 21), Cronin advocates for transdisciplinary approaches to be adopted by scholars. This could usefully lead to a re-evaluation of the role of translation and translators in the age of technologization through collaboration with community members and organizations. In this way, Cronin argues, Translation Studies can participate in the critical dialogue at a time of environmental crises brought about by the Anthropocene era. In summary This volume sets out to discuss translation and technology as a growing yet disruptive relationship. Together the contributors paint a picture of a profession or an activity that is dynamic and plays important social and ecological roles, sustaining global communication needs for businesses and individuals in public and private spheres. The examples discussed in the volume span NMT, post-editing, ROM hacking, crisis translation in disaster settings, media accessibility and interpreting at a distance for the Deaf community, to name a few. The volume highlights the central position technologies are occupying in translation and in some interpreting practices while drawing the reader’s attention to human agency. In all this, as already acknowledged by TS scholars, translation continues to defy an exact definition (Williams 2013: 5–9) and technological factors are only confirming the multiplicity of the practice and concept of translation. From a practising translator’s perspective, Mark Polizzotti (2018: xi) describes in his Sympathy for the traitor: a translation manifesto the nature of translation work as ambivalent, ‘skirt[ing] the boundaries between art and craft, originality and replication, altruism and commerce, genius and hack work’. His manifesto celebrates the variability of human translation and defends the oft-used analogy of a translator as a traitor in the sense that translation decisions are not always deducible from the words in the source text alone. The challenge for ‘augmented translation’ or any other advanced technology-mediated environment would therefore be to facilitate such a complex, ill-defined human decision-making process. The inquiry into the deepening connection between translation and technology, and also translation by the human and by the machine, will widen the scope for the future development of Translation Studies and the translation profession, as the contributors of this volume eloquently demonstrate. In the spirit of participatory culture, the more stakeholders who partake in the examination of what is happening with the human–machine unison or abrasion in contemporary translation, the more chance we have of grappling with the changes taking place. It is hoped that the diverse observations presented in this volume will provide a fresh impetus for theory building for scholars, which will enable translators to better navigate increasingly technologized environments that are complex, unpredictable and fragile. This in turn will help us ensure the survival and sustainable evolution of translation, in the advent of algorithm-led intelligence. Finally, the reference to ‘entanglement’ in the title of this introduction is borrowed from quantum physics. Described by Einstein as ‘spooky action at a distance’, quantum entanglement refers to the phenomenon where particles separated in space and time are inextricably linked (de Ronde and Massuri 2018). This deep-seated correlation and the synched status of two entities evokes the inescapable bond being formed between the human and the machine. It could be the vision for the future of the refined, if inscrutable, art of translation with human and machine learning enriching each other. This is ultimately related to the question of what it is to be human and a translator in the technologizing age. Standards for the language, translation and localization industry Sue Ellen Wright Introduction This chapter addresses the role of standards and standardization treating language, language resources of many kinds, and the global language enterprise. It traces their evolution from their inception as terminology standards to a 21st century environment where language and the content that it expresses are chunked and identified for efficient retrieval and reuse, with the goal of creating not just documents, but rather knowledge systems where relevant ontologies, terminology collections, source-target bitexts (e.g., translation memories), or other knowledge objects provide a coherent and holistic basis for the information life cycle rather than being limited to the one-off document instances common in the past. Standards comprise documents that provide requirements, specifications, guidelines or characteristics that can be used consistently to ensure that materials, products, processes and services are fit for their purpose: An International Standard provides rules, guidelines or characteristics for activities or for their results, aimed at achieving the optimum degree of order in a given context. It can take many forms. Apart from product standards, other examples include: test methods, codes of practice, guideline standards and management systems standards. (ISO 2018b) This chapter will also treat standards and ancillary resources from beyond the ISO framework that nonetheless fit this profile. Describing the environment around language standards starts with the methodology for creating industrial standards. Although approaches vary somewhat by region and domain, the fundamental principle involves collaboration among technical experts in committee working groups to achieve consensus on process, procedures, product specifications, and terminology used in creating and evaluating goods and services. Most standards are elaborated by national, regional, and international standards organizations under the aegis of the International Organization for Standardization (ISO, see below), although some ‛industrial standards’ are created by industry-based representatives of corporate members of professional organizations outside the ISO context. In both models, establishing ‘consensus’ involves the proposal of draft specifications followed by revisions and refinements until objections are resolved and agreement is reached. The process is never easy, especially on the international scale, where ‘global relevance’ can be hard come by, and often takes three to five years. The main section of this chapter treats both the historical development of the standards and literature documenting standards development as a continuum, inasmuch as the literature is primarily descriptive and reflects the state-of-the-art at any relevant point in time. Literature review and historical trajectory General literature Literature will be reviewed in sync with standards development, bearing in mind that many years passed before anyone actually wrote about language standards as such. References abound on the web, but readers should be cautioned that all but the most up-to-date articles, book chapters, and webpages treating the language enterprise should be viewed with a certain scepticism because articles quickly become outdated, and industry resources often fail to refresh their information with any frequency. Throughout this chapter, the term language enterprise refers to the totality of language documentation, manipulation, and exploitation wherever it occurs: in industry at large, in the language industry in particular, in government at all levels, in academia, and elsewhere in the public and private sectors. References will be made to specific ISO, CEN, ASTM, DIN,1 and other standards, for which full title and publication dates can be found in the Language Standards Organized by Topic list at the end of this chapter. Language-related standards for and as industrial standards At its core, most human activity involves language, be it spoken or written, actual ‘in-person’ communication or recorded in some medium – etched in stone, carved in clay, inscribed in ink on velum or paper, or digitized electronically. Initial efforts to normalize language per se featured valorization of specific language variants, e.g., in the European context, the evolution of dictionaries (English and German) and of national Academies of language (French and Spanish), resulting in conventionalized spellings and grammars. Milestones such as Samuel Johnson’s Dictionary of the English Language (1755) and François I’s Ordonnance de Villers-Cotterêts declaring that French would henceforth be the official language of the courts (1539) stand out on such a timeline. Interesting as these events may be to linguists, they concern general language, while this chapter will focus on language used in industrial standards, and then on the standardization of methods, processes and even products related to the emergence of the language enterprise itself as an industry. Standards governing products and processes date from the beginnings of measurement systems: the Egyptian ell as a length measurement, for instance, or the calendar systems of the ancient world established consensus in specific areas in order to ensure effective human collaboration (ANSI 2018). Standardization of products, processes, test methods and materials specifications can be traced through the medieval guild system or even earlier, and the earliest formal international effort of significance probably involved the International Bureau of Weights and Measures, which began in 1875 to establish standard measures and units, resulting in The Treaty of the Metre and eventually, today’s International System of Units (SI; Page and Vigoureux 1975, BIPM 1875 to 20182). While not a ‘terminology standard’ per se, this list of units and values did pave the way for dedicated standardization in response, not surprisingly, to industrial advances later in the 19th century: the introduction of uniform rail gauges in the US and Europe, for instance, followed in the histories by the elaboration of standards for fire-fighting equipment in the early 20th century (ANSI 2018, Wright 2006a, ASTM 2018). Formed in 1898, the International Association for Testing and Materials became the American Society for Testing and Materials (now ASTM International), based on the principle of consensus standards. ASTM is now one of ca. 300 individual standards bodies under the umbrella of ANSI, the American National Standards Institute. Other national groups count their origins in the same general time period: The Engineering Standards Committee in London, 1901, became the British Standards Institution by 1931 (BSI 2018); the Normenausschuß der deutschen Industrie, founded in 1917, evolved into today’s Deutsche Institut für Normung (DIN, Luxbacher 2017); and AFNOR, l’Association Française de Normalization, can be traced back to 1901 (AFNOR 2018). On the international level, the International Electrotechnical Commission (IEC 2018a) was founded in London in 1906 (IEC 2018c), followed by the International Federation of the National Standardizing Associations (ISA), which originated in 1926, was disbanded in 1942, and reorganized in 1946 after World War II as ISO, the International Organization for Standardization, occupying today the position of global umbrella organization for 161 national standardizing bodies such as those cited above. CEN, the European Committee for Standardization or Centre Européen de Normalisation came together in 1961 to unify the standards organizations in the European Union, although today ISO standards frequently supplant CEN (OEV 2018). Foundation and build-up Early language standards defined terms used in product and process standards. The earliest documented terminology standard was ASTM D9, Standard Terminology Relating to Wood and Wood-based Products, published in 1907 (Ellis 1988). ASTM interest in standardizing terminology led to formation of a separate committee (E-8) in 1920 for Nomenclature and Definitions, followed in 1978 by the Committee on Terminology (CoT), which created a standardized practice for defining terms and a Compilation of ASTM Standard Definitions. The CoT was disbanded after its role was essentially absorbed into the US mirror committee for ISO TC 37. On a global scale, the International Electrotechnical Commission (IEC), founded in 1906, published its International Electrotechnical Vocabulary (IEV) in 1938, whose direct descendent is today’s Electropedia (IEC 2018a). Under the leadership of Eugen Wüster, the founding theorist of terminology studies, ISA/ISO established Technical Committee TC 37, which, despite a hiatus during the WWII years, continues today as ISO TC 37, Language and Terminology (Trojar 2017: 56, Campo 2013: 20 ff.), with some name changes on the way – Terminology (principles and coordination) (1951); Terminology and other language resources (2003); and Terminology and other content and language resources (2006). In his history of Infoterm, the ASI administrative entity governing TC 37, Galinski categorizes the period from 1951 to 1971 as the ‘foundation phase’, which focused primarily on the theory of terminology and terminology work (later called terminography), the compilation of terms and definitions for the ‘vocabulary of terminology’, and guidance for preparing ‘classified vocabularies’. The audience for these standards was originally limited to standardizers identifying and defining terms used in their standards. Terminology documentation during this era involved the handwritten (or possibly typed) collection and documentation of terminology on paper fiches (Felber 1984: 162 ff., Wright 2001: 573). Through the mid-1980s, TC 37 focused on defining concepts and terms from an onomasiological, that is, concept-oriented, systematic standpoint. Then as now, the core standard, ISO 704, and its companion vocabulary, ISO 1087, reflected Wüster’s General Theory of Terminology, which is firmly grounded in a long tradition of linguistic theory culminating in the work of Saussure and Frege. ISO 639 had already originated in 1967 as a repository of ‘symbols’ for languages, the beginning of the two-letter language codes (Galinski 2004a & b, Martincic 1997). Consolidation, outreach, and the digital turn Galinski calls his second phase (1972–1988) the Consolidation Phase, and focuses on outreach to other sub-disciplines, not only chronicling the maturation of TC 37 within ISO, but also mirroring a turn in the evolution of the overall language enterprise. By the late 1980s, work had been organized into three sub-committees: SC 1, Principles and methods; SC 2, Terminology work; and SC 3, Computer applications in terminology, with 704 and 1087 assigned to SC 1, and language codes and a new ISO 1951 for symbols used in lexicography assigned to SC 2. Sub-Committee 3, however, ushers in a paradigm shift in the role of language in standardization, or perhaps more accurately, of standardization with respect to language: in 1987 ISO 6156, Magnetic Tape Exchange Format for Terminological / Lexicographical records (MATER) appears. Writing almost two decades later, Briggs notes a phenomenon perhaps little understood in the early 1980s: ‘Standards transform inventions into commercial markets’ (Briggs 2004). Briggs points out that the invention of the radio and of flight did not actually trigger new industries until such time as the standards to regulate and actually enable these industries had been put in place. The MATER standard is the first in a growing flow of enabling standards (see Wright 2006b: 266 ff.), which is to say, standards that facilitate functions that would be impossible without the standard itself. In the case of MATER, this task involves the exchange of terminological (and theoretically lexicographical) data, heralding the advent of large-scale main-frame-based terminology management systems such as the Siemens TEAM terminology management system. Subject to a long evolution over the course of the 1980s, this one document was the first in a series of standards, both inside and outside ISO TC 37 designed to ensure data compatibility and interchangeability in a nascent industry. ISO 6156 itself did not enjoy a long-term or widespread reception – it was uniquely introduced to meet the needs of the TEAM system, whereby the parent database resided on a main-frame computer in Munich, but translators and terminologists working in far-flung Siemens centres around the globe provided data maintenance information and received updates either on tape or via daily overnight data transfers over dedicated phone lines. Nonetheless, the standard did not focus on a virtual data stream, but rather on data stored on magnetic tape.3 The seminal ideas in the MATER serialization included: 1) the sharing, exchange, and interoperability of data, 2) that is configured in a series of records, each devoted to a single concept-oriented term entry, 3) consisting of discrete data fields identified by data category names (e.g., English term, English source, etc.) and 4) the use of standardized encoding to mark up these data elements in an otherwise undifferentiated flat data flow. These criteria still apply, although the details of the successor standards have changed. It is important to note some critical developments that occurred on the way to that 1987 MATER publication date. While standardizers were focused on mainframe-based terminological systems, events unfolding elsewhere were destined to doom MATER to a limited life span. The IBM PC appeared in 1981, signalling the beginning of the end of main-frame dominance and stabilizing the then fragmented personal computer market. The availability of an affordable, increasingly user-friendly platform for text authors, translators, and terminologists introduced a spate of word processing applications, as well as terminology management, and eventually translation memory, systems that inspired an interchange format for ‘micro’-computers – the so-called MicroMATER standard (Melby 1991). Along these lines, the publication in 1986 by ISO/IEC JTC 1/SC 34 for Document description and processing languages, of a Standard Generalized Markup Language (SGML), after an extended period of development, was also the harbinger of another significant paradigm shift that impacted knowledge and information representation and sharing, academic research environments, as well as commerce and industry, on a global scale. In fact, although MicroMATER did not begin as an SGML formalism, it parallels many features from SGML, which offered a powerful medium not just for marking up presentational features in text, but also for creating standardized encoding systems for managing corpora and other text-based data resources. This new medium inspired computational linguists to come together on an international scale, beginning in 1987, to establish the Text Encoding Initiative (TEI 2018a, 2018b). Extension phase (1989–1996) Sticking with Galinski’s time frames, which justifiably feature Infoterm activities, this chapter also views a broader spectrum of standards from the period. It almost goes without saying that SGML begat HTML, and HTML begat XML, and even if it was not always good, the whole development was prolific, fostering the World Wide Web as we know it today. Much of this activity involves manipulating language and informational content in some form, thus extending the arena of language standardization far beyond TC 37. The TEI project originally expanded the use of SGML markup into a wide variety of corpus-related approaches, in addition to lexicography and terminology. Indeed, the simmering MicroMATER bubbled over into TEI-Term (TEI 1994/1999) and eventually was re-published in 1999 as ISO 12200: Computer applications in terminology – Machine-readable terminology interchange format (MARTIF) – Negotiated interchange and its companion standard, ISO 12620: Data categories. Other TEI-related formats found their way into the ISO framework via the formation of TC 37/SC 4 in 2001/2002, focusing on Language Resource Management, specifically: feature structures, lexical markup, semantic annotation frameworks, and metadata infrastructures, among other topics (ISO 2018a). Extending the language codes and beyond Identifying languages with short codes (treated as symbols in ISO) relies on a foundational family of language standards, which over time has engaged a growing set of stakeholders, including standardizers, lexicographers and terminologists in TC 37; librarians and documentation specialists in TC 46 (Information and Documentation); Bible translators motivated to codify all human languages in their missionary work (Summer Institute of Linguistics or SIL); internet engineers and metadata experts associated with the Worldwide Web Consortium (W3C) desirous of stable, unchanging language-specific character code points; and most recently, ethnographically oriented field linguists, who have come to question an earlier lack of scholarly insight, coupled with conflicting stakeholder perspectives rather than more scientific linguistic, cultural and historical realities, particularly in the case of so-called ‘long-tail languages’.4 The initial ISO 639 developed by terminologists served as the progenitor to the current Part I two-letter codes for languages (en, de, fr). This approach arose out of the constrained paper fiche environment of terminographic practice before the digital age. The early PC, with its claustrophobic 360K floppy disks and limited RAM capacity, also boxed developers into a parsimonious language ID regime, reflected in the highly economical MicroMATER model. Unfortunately, however, the two-letter codes pose one insurmountable handicap: the mathematical permutation of the number two coupled with the 7- and 8-bit limitations of the character codes. Library scientists at the Library of Congress, also confined within the boundaries of paper fiches – the then common library catalogue cards – introduced three-letter codes for use in their MARC (MAchine Readable Cataloguing) record (LoC 2009). The advantage of three letters is that they provide more satisfying mnemonics as well as enough code points to accommodate realistic projections of the world’s identified languages, but the library-oriented system is nonetheless administratively limited to languages that produce sufficient documents to be catalogued. SIL International, which has its origins in the Summer Institute of Linguistics, started with a primary focus on Bible translation, later branching out to support literacy in a wide range of language communities. As a consequence, the SIL three-letter codes (eng, deu, fra) have been motivated towards total inclusion, resulting in July 2018 in a total of 7097 languages (SIL 2018a & b). Over time, the language codes have, however, played an integral role not only in identifying languages, but also in pinpointing locale-related information types. In parallel to the language codes, ISO 3166-1:2013, Codes for the representation of names of countries and their subdivisions is maintained by the ISO 3166 Maintenance Agency, which consists of a set of prominent ISO Participating Member bodies, along with a few international entities, including the United Nations Economic Commission of Europe, the Universal Postal Union, and the Internet Corporation for Assigned Names and Numbers (ICANN). The list provides alpha-2 and alpha-3 country code elements as well as numeric country codes assigned by the UN and comprises essentially those countries that are recognized as member bodies of the UN. The final piece in this particular encoding puzzle is ISO 15924, which provides four letter codes for the names of scripts and is maintained by an ISO Registration Authority under the aegis of UNICODE. Where SGML (Standard Generalized Markup Language) and its web-enabling descendent HTML reported language identity as values of the encoding attribute lang, XML’s xml:lang attribute introduces a detailed sequence of codes based on a combination of the language symbols with country and script codes to report very specifically on the locale and character set of a document. Typical examples of combined locale codes might be fr-CA (French as spoken in Canada) or zh-Hant-TW (modern Chinese printed in traditional script as used in Taiwan). This composite form makes up a language tag (Ishida 2014, IETF BCP 47). In addition to their use in the language tags described here, the country codes also form the basis for country-specific geographical Top-Level Domains (TLDs) on the web (Technopedia 2018). Character encoding The digital turn cited above produced a broad spectrum of language-related standards that had been developing over the course of the mid-century. In the beginning, character representation in computer environments was sketchy at best. Some scripts did not even exist yet – a truly legible Arabic font, for instance, had to be designed in order to process the language with first-generation terminology applications, and a script encoding developed for one platform would in all likelihood be unintelligible if data were transferred to another environment. The ASCII code (American Standard Code for Information Interchange, sometimes referred to simply as ‘ANSI 1981’), notorious today for its 128-character limitations, nevertheless represented a step forward because it provided a compliant 7-bit realization of both lowercase and uppercase text for English (Brandel 1999). ASCII was extended first by 8-bit upper-ASCII, which accommodated most Latin-character languages, but was not truly implemented in much software until the mid-1980s. Augmented by the extensive ISO 8859 family of standards (8-bit single-byte coded graphic character sets, which Michael Everson has characterized, however, as a ‘font hack’ (NPR 2003) because they simply re-assign character fonts to the same few actual code points), they accommodated most requirements for Latin-character languages as well as mappings to other alphabetic scripts, such as Greek and Cyrillic, but with limitations – one could not use much more than one set of characters at a time because there were still only a limited number of usable code points. By the early 2000s, however, development in the 8859 space ceased (2004), followed by full web adoption of Unicode (ISO 10646) in 2007. Detailed description of these character encoding systems exceeds the scope of this chapter, but it is worth noting that the Basic Multilingual Plain of UNICODE, UTF-8, provides 55,029 characters (as opposed to 128/256) and accounts for so-called Unified Han, including Japanese Kanji and Simplified Chinese, followed in 2006 by introduction of the second plain, UTF-16, to accommodate the complete Chinese encoding, not to mention a seemingly endless proliferation of emojis. Unicode provides ‘a unique number for every character, no matter what the platform, no matter what the program, no matter what the language’ (Unicode 2018a). UTF-8 has become the character encoding scheme of choice on the web and in numerous other platforms, especially for the accommodation of multiple languages where non-English orthographic conventions require Latin letters with diacritics, additional punctuation marks, and non-Latin character sets. POSIX and the CLDR (Unicode Common Locale Data Repository) Language and locale information provided by the extended xml:lang does not, however, fully cover all the cultural and regional information related to programming and web environments. The IEEE (ISO/IEC 9945) Portable Operating System Interface (POSIX), begun in the late 1980s, supported compatibility between operating systems. By 2001–2004, POSIX had added locale definitions, including a ‘subset of a user’s environment that depends on language and culture conventions’, which accommodated character classification and conversion, collating order, monetary information, non-monetary number representation, date and time formats, and messaging formats (IEEE 2018). Currently locale detail is documented using the Unicode Common Locale Data Repository (CLDR), including language tags, calendar style, currency, collation type, emoji presentation style, first day of week, hour cycle, line break style, word segmentation, number system, time zone, and POSIX legacy variants, in addition to transliteration guidelines (ICU; UNICODE 2018b). Language codes and CLDR notation are intimately embedded throughout the web and encoded in computer programs, resulting in an entailment that vigorously resists any trivial change, rendering the web community a powerful stakeholder that advocates for the status quo. Consequently, in the early 2010s, when socio-linguists proposed completely revamping the codes to eliminate legacy errors and to reflect modern scientific definitions of language and language variants, the UNICODE community, supported by IETF, the W3C, and the CLDR initiative, asserted the need for stability and continuity in the current system (Morey and Friedman 2013). The Maintenance Authority does, however, correct errors on a regular basis. The language industry and the translation extension Galinski categorizes 1989 to 2003 as ‘extension’ years, citing SC 3’s move into computer-supported terminology management and the founding of SC 4 in 2002. This period marks a definite turn towards translation-oriented topics. Galinski’s terminal date of 2003 is artificial, however, because, although it marks a milestone for Infoterm, it does not map well to overall standards evolution. During this period, industry-oriented standards activity outside the framework of the national and international standards bodies began. It is tempting to select 2008/2009 as a critical intermediate juncture, because there is somewhat of a break in the documentation of standards development at this point, but this blip is more relevant to the global financial crisis than to the development of translation and localization industry standards. 2012 makes more sense because this year sees the creation of TC 37 SC 5 for Translation, Interpreting and Related Technology in 2012. To understand these trends in language standards, it is important to look back at the creation of the Internet, followed by the appearance of the web, which was accompanied by the growth of multiple layers of enabling technologies supporting public, academic, and commercial computing environments. Over this time and in a number of venues, experts from a variety of major web technology companies came together to form organizations and associations to achieve consensus on best practices, particularly with regard to the translation and localization of information, computer programs, and web content. Often, their projects reflect pragmatic efforts to achieve consensus-based solutions more rapidly than is sometimes the norm in the formal ISO process. Localization Industry Standards Association (LISA) LISA was founded in Switzerland in 1990, but only survived for a little over two decades, dissolving in 2011. LISA served as a professional and trade organization for companies, translators, language engineers, and other experts involved in the translation and adaptation of software and web content into a variety of languages. LISA hosted global conferences and promoted the interests of globalization and internationalization in the context of the localization industry, in addition to supporting the elaboration of a family of XML-based standards. For purposes of this discussion, localization involves the adaptation and transcreation involved in configuring a translated resource (computer program, web page, or even a concrete product like an automobile) to the expectations of a target language and culture (see Folaron in this volume). At the time of its demise, the list of LISA standards included (LISA 2004, IEC 2015): TBX: Term Base eXchange (TBX); TBX: TBX-Basic (the first industry-supported public dialect of TBX); TMX: Translation Memory eXchange (TMX); SRX: Segmentation Rules eXchange (SRX); GMX: Global information management Metrics eXchange ; xml:tm: XML text memory for storing text and translation memory in XML documents; Term Link: A lightweight standard for linking XML documents to terminology resources. Roturier in this volume provides a thorough discussion of these standards, with the exception of the last two, whose functions have essentially been subsumed in the OASIS-based XLIFF standard. As noted in the earlier chapter, the rights to these standards were turned over to ETSI, the European Telecommunications Standards Institute, where they were further elaborated until August of 2015, when ETSI discontinued the assigned Special Interest Group. This move probably reflects lack of buy-in from major industry players, who by then had made a significant commitment to OASIS. The TBX standard, together with its TBX-Basic dialect, had, however, already been ceded to ISO as ISO 30042, so it has continued development under the auspices of ISO TC 37/SC 3 and the TerminOrgs group, Terminology for Large Organizations, ‘a consortium of terminologists who promote terminology management as an essential part of corporate identity, content development, content management, and global communications in large organizations’ (TerminOrgs 2018). TMX, SRX, and GMX remain on the ETSI website for download, but are unfortunately not currently being updated. Efforts to resolve copyright issues between the OASIS/XLIFF group and ETSI have, at least for the present, failed, which may well have been a bad move on the part of ETSI, as all indicators point to a robust, comprehensive XLIFF as the standard of the future. LISA also developed a Best Practice Guide and a Quality Assurance (QA) Model, marketed as a computer utility, but also incorporated into some computer-assisted translation systems (Melby et al. 2001, LISA 2004). OASIS Founded in 1993, OASIS (the Organization for the Advancement of Structured Information Standards) is an international association devoted to developing guides and standards for use in XML environments, both on the web and across networks. The emphasis of many OASIS standards is on text and information processing and interoperability. The great strengths of OASIS are the broad range of its standards and the active participation of a wide spectrum of major industry partners, not just from the web and computational environment, but also from manufacturing, government, and academia. Many collaboration projects also exist between OASIS committees and national, regional, and international standards bodies. OASIS is the home of DITA and of XLIFF (OASIS 2018), as recounted in Chapter 3. Analysis for this phase Leading up to 2008, articles and reports began to appear treating standards in the language industry. Already in 2003/2004, in a now inaccessible web-based report, Wright asserted that the digitization of text, accompanied by the ability to chunk and identify strings and segments, enabled the creation of data objects using embedded (or for that matter, stand-off) metadata and the automatic processing and manipulation of text by marking it for semantic content, thus facilitating the exploitation of language as both process and product. This trend transformed the traditional document production chain into what is understood today as a feedback-rich information life cycle, supporting data migration, conversion, re-importation and reutilization on a global scale. Wright’s study identified XML and web-related standards, such as RDF, ISO 11179 (metadata registries), OMG’s UML standard, UNICODE, and TC 46 activities surrounding controlled vocabularies (now ISO 25964), along with a number of W3C protocols, as a core stratum of standards supporting the information life cycle. On this layer there resides a family of XML tools (many of which started out in the SGML environment) that was revolutionizing the authoring function, leading eventually to standards governing language mediation. OASIS’ XLIFF (2003) already commanded attention in the translation area, as did LISA’s TMX and segmentation standards, as well as controlled language solutions such as AECMA Simplified English. This list includes, in addition to LISA/OSCAR XML standards, W3C’s OWL Web Ontology Language and a number of now defunct initiatives such as OLIF (Open Lexicon Interchange Format) for terminology interchange, along with Topic Maps and DAML-OIL. Under the heading of Corpora, Wright referenced the then active EAGLES project and TEI, precursors of TC 37/SC 4’s mission. Toward the end of her report, Wright stated, ‘…formats like RDF, with its promise to revolutionize the Web in the direction of the visionary Semantic Web, have not yet met their full potential, but as more and more implementations are put in place, the power of this approach will increase momentum in this direction’. She predicted the implementation of full UNICODE compliance and ‘semi-automated authoring environments’, which she surmised would facilitate the embedding of semantic markers in text. She also expressed concern that TBX would be unlikely to gain wide acceptance until such time as it could achieve ‘black-box turnkey status’ without the user necessarily understanding how it works. 2004 was evidently the year of the private industry report, since Wright also created a second such report where she classified language standards according to a ‘translation pyramid’, a five-sided model with the translator at its base, supporting four elements: Text, Task, Tools and Training. Here she viewed the then prevalent normative trends as facets of incipient efforts to apply standardization approaches to translation and localization. Drawing on a background in manufacturing and engineering, she foreshadowed the later trend to apply the ISO 9000 Total Quality Management approach to the assessment of translation product and the evaluation of translators. This then relatively novel approach was melded with notions of equivalence and adequacy, which were surfacing in translation studies, stressing the affinity of the target-oriented translation brief to the ISO 9000 principle that quality is a function of client specifications. She visualized ISO 9000 as a top-level umbrella standard over-arching a level 2 (industry-specific certification standards, then reflected by ASTM F2575 and CEN 15038); level 3 (industry-specific metrics, reflected at the time in SAE J2540 and the ATA Framework for Error Marking); and level 4 (enterprise-specific customized procedures and metrics). This report also invokes the notion of Failure Mode and Effect Analysis (FMEA), an essential risk-management component of the ISO 9000 toolkit that is indeed being applied today as part of quality certification for Translation Service Providers. She drew an analogy between the FMEA Critical Items List (CIL) to quality metrics error categories, the ATA Framework for Error Marking, and the LISA Quality Metrics project, pointing to existing or proposed industry standards related to her four major criteria (See also Wright 2013 and 2017). In 2006, Lommel also focused on the role of digitization in the form of XML serialization used to transform human knowledge into ‘information as a business commodity’ (Lommel 2006: 225). Like Wright, he outlined a model whereby units of knowledge marked as units of text are abstracted into reusable and processable information units that can be repurposed on an industrial scale, which essentially describes information-centric environments where content is managed for a range of potential applications. In this context, he also described the then evolving LISA XML standards and stressed the advantage of vendor independence, collaborative environments, and data interchangeability in XML-empowered scenarios. Translation standards Previous discussion has already set the stage for initial efforts to integrate language mediation activities into the environment of industrial standards. Certification and service standards Work began in the late 1990s on ISO 12616: Translation-oriented Terminology (published in 2005), which may have been originally perceived to be a one-off extension of the SC 2 mandate to elaborate terminography-related standards. Along these lines, the translation industry did find a home in TC 37 in 2005, when a Working Group for Translation and Interpreting Processes was formed in SC 2. By 2012, activity migrated to a new SC 5, Translation, Interpreting and Related Technology. DIN issued a standard on translation contracts in the late 1990s, with ASTM following in 2003 and 2005 with standards on interpretation practice and requirements as well as customer-oriented guidelines for negotiating translation and localization specifications. CEN worked in parallel, publishing their EN 15038: Translation services: Service requirements in 2006. Today, the most prominent of the translation standards, is ISO 17100:2015: Translation services – Requirements for translation services/Amd 1:2017. This standard has now become the lynchpin document for the certification of translators and translation service providers in the ISO 9000 environment, and it embodies the difficulty of establishing consensus on an international scale when confronted by contextual and linguistic differences across cultures. The original unamended standard published in 2015 stated that translators shall have earned ‘a graduate qualification in translation’ [emphasis for purposes of this discussion], which on the face of it may sound like a good idea, but in North America, this wording means 1) a Master of Arts degree at least, and 2) a degree that has ‘Translation’ in the title of the concentration. The intent of the drafters of the passage, however, was that translators should have graduated with an initial degree from a university, that is, with a baccalaureate degree. It took two years of arguing for the working group to accept the fact that it would be easier to change the wording in the standard than to change extensive usage by native speakers of the English language. The second problem was that although degrees ‘in Translation’ are common in Europe and in European languages in North America, they are not readily available in some countries – Japan, for instance, and for some languages. Hence the amended 2017 wording: ‘a) has obtained a degree in translation, linguistics or language studies or an equivalent degree that includes significant translation training, from a recognized institution of higher education; b) has obtained a degree in any other field from a recognized institution of higher education and has the equivalent of two years of full-time professional experience in translating’. These requirements, along with an option for five years of full-time professional experience, pose problems, however, for speakers of long-tail languages who do not have access to formal curricula or who rarely have enough work to qualify as full-time translators. In its broader programme, SC 5 maintains an ongoing commitment to more specialized standards in translation and interpreting, specifically for community, legal and health-related interpreting. One major effort focuses on a set of standards for various types of interpreting equipment (see Braun in this volume), with an upcoming trend focusing on requirements for leading-edge mobile systems. Emerging issues and outlook Important examples of new initiatives include the following. In SC 2, efforts are progressing to define criteria for linguistically motivated classification of language variants in ISO 21636: Identification and description of language varieties, which will define critical metadata values (data categories) for the exchange of secondary information as well as to support the re-usability of language resources. This activity addresses Morey’s criticisms cited above (Morey and Friedman 2013) of inadequacies inherent in the language codes without disturbing the utility of the current system. DIN in conjunction with German tekom has submitted a work item on the Vocabulary of Technical Communications, which has led to the formation of a new TC 37 subcommittee for dealing with linguistic (as opposed to product-related) aspects of technical communication. Initial discussion of the standard places this terminology management activity firmly in the framework of the product information life cycle (Klump 2018). A set of standards initiated by SC 4 features text markup for the creation of annotation schemes for linking semantic and syntactic elements in text to the generation of technical diagrams (ISO24627-3). The new standard is associated with existing SC 4 annotation models, but has the potential to interact with both SC 1 efforts to diagram concept structures as well as to integrate data-driven diagramming graphics into technical texts. Although the scope is currently limited, future directions might lead to standards for computer-generated scientific diagrams. SC 3, partly with its ISO 12620 Data Category standard and the creation of the www.datcatinfo.net data category repository has long maintained a relationship with ISO/IEC JTC 1/SC 32 for metadata, patterning their work after standards for data element dictionaries. A new liaison with IEC SC3D: Product properties and classes and their identification expands on the definition of metadata elements in the context of the International Electrotechnical Commission, IEC 61360 – Common Data Dictionary (IEC 2018b). These moves into technical communications, on the one hand, and metadata, on the other, reflect a holistic trend toward enterprise-wide document production and information management. Emboldened by some past success in predicting future directions for language standards, one might venture to suggest that this tendency will grow as standardizers try to support information management by integrating workflows and resources throughout enterprise processes. This trend can already be seen in the linkage between XLIFF, ITS and TBX, for instance. This kind of through-composed approach to technical writing, translation, localization and information management would ideally involve the coordination of standards cited here with other special vocabulary standards, such as the SKOS (Simple Knowledge Organization System) from ISO Technical Committee 46, in order to create tools for linking data both within enterprises and across the web in support not only of translation and localization, but also of writing and information retrieval from massive bitext and multilingual text resources (SKOS 2018). Although building blocks are in place, future developments must of necessity lead in new directions to bind existing language standards with work going on in the W3C and elsewhere. ASTM is elaborating a work item, provisionally called ASTM WK 46396, designed to standardize the top level of DFKI’s EU-funded iteration of the old LISA Multidimensional Quality Metrics (MQM) (see Doherty and Pym’s respective chapters in this volume, Lommel et al. 2015, Burchardt 2016, Görig 2017), leaving the rich detail of the subsequent layers to be presented as ancillary material in a flexible web-based environment (Lommel et al. 2015). In a parallel activity, ASTM is also developing a standard for holistic evaluation of published translations intended as a kind of triage activity designed to reveal when published translations are unacceptable to the target audience. A similar more recent ISO activity, ISO 21999, has the intent of introducing an as-yet-undefined quality metric. These standards add a critical product-oriented component to the process-oriented QA environment comprising the application ISO-9001 plus ISO 17100. Conclusion This chapter chronicles a progression from engineer-linguists like Eugen Wüster, who inspired engineers standardizing vocabulary to serve the purpose of engineering-oriented standardization, to linguists applying computer engineering principles for the purpose of controlling and analysing language, and ultimately, of engineering knowledge, giving us today’s notion of knowledge and language engineering. It begins with the efforts of terminologists, lexicographers, librarians and field linguists to normalize language names and word-related data categories, but then segues into standards that enable tools for a burgeoning translation/localization industry. It demonstrates the evolutionary trajectory from modest efforts to define small sets of highly specialized terms to culminate in information-oriented environments where standards-driven linguistic markup enables the transformation of surface-level word and character strings into semantically encoded terminological knowledge units populating massive data stores in the form of text and bitext corpora, supporting multilingual retrieval, text processing and further manipulation. Standards, Bodies and Related Acronyms AECMA, European Association of Aerospace Industries AFNOR, Association française de normalisation ANSI, American National Standards Institute ASCII, American Standard Code for Information Exchange ASTM International [Originally: American Society for Testing and Materials] BIPM, International Bureau of Weights and Measures BSI, British Standards Institution CEN, Comité européen de normalisation CLDR, (Unicode) Common Locale Data Repository CoT, Committee on Terminology (ASTM) DFKI-MQM, Deutsches Forschungsinstitut für künstliche Intelligenz-Multidimensional Quality Metrics DIN, Deutsche Institut für Normung ETSI, European Telecommunications Standards Institute ICANN, Internet Corporation for Assigned Names and Numbers ICU, International Components for Unicode IEC, International Electrotechnical Commission IETF, Internet Engineering Taskforce ISO, International Organization for Standardization ISO/IEC JTC1, ISO/IEC Joint Technical Committee 1 JTC, Joint Technical Committee LISA, Localization Industry Standards Association LoC, (US) Library of Congress MARC, Machine Readable Cataloguing OASIS, Organization for the Advancement of Structured Information Systems OMG, Object Management Group POSIX, Portable Operating System Interface SC, Sub-Committee SI Units, International System of Units SIL International, originally Summer Institute of Linguistics TC, Technical Committee TEI, Text Encoding Initiative UNICODE, UTF, Unicode Transformation Format W3C, Worldwide Web Consortium WG, Work Group, Working Group
TT-Assig #4 – see attached
3 1 THE DEVELOPMENT OF TRANSLATION TECHNOLOGY 1967–2013 Chan Sin-wai the chinese university ff hfng kfng, hfng kfng, china Introduction The history of translation technology, or more specifically computer-aided translation, is short, but its development is fast. It is generally recognized that the failure of machine translation in the 1960s led to the emergence of computer-aided translation. The development of computer- aided translation from its beginning in 1967 as a result of the infamous ALPAC report (1966) to 2013, totalling 46 years, can be divided into four periods. The first period, which goes from 1967 to 1983, is a period of germination. The second period, covering the years between 1984 and 1993, is a period of steady growth. The third period, which is from 1993 to 2003, is a decade of rapid growth. The last period, which includes the years from 2004 to 2013, is a period of global development. 1967–1983: A period of germination Computer-aided translation, as mentioned above, came from machine translation, while machine translation resulted from the invention of computers. Machine translation had made considerable progress in a number of countries from the time the first computer, ENIAC, was invented in 1946. Several events before the ALPAC report in 1966 are worth noting. In 1947, one year after the invention of the computer, Warren Weaver, President of the Rockefeller Foundation and Andrew D. Booth of Birkbeck College, London University, were the first two scholars who proposed to make use of the newly invented computer to translate natural languages (Chan 2004: 290−291). In 1949, Warren Weaver wrote a memorandum for peer review outlining the prospects of machine translation, known in history as ‘Weaver’s Memorandum’. In 1952, Yehoshua Bar-Hillel held the first conference on machine translation at the Massachusetts Institute of Technology, and some of the papers were compiled by William N. Locke and Andrew D. Booth into an anthology entitled Machine Translation of Languages: Fourteen Essays, the first book on machine translation (Locke and Booth 1955). In 1954, Leon Dostert of Georgetown University and Peter Sheridan of IBM used the IBM701 machine to make a public demonstration of the translation of Russian sentences into English, which marked a milestone in machine translation (Hutchins 1999: 1−16; Chan 2004: 4 125−226). In the same year, the inaugural issue of Mechanical Translation, the first journal in the field of machine translation, was published by the Massachusetts Institute of Technology (Yngve 2000: 50−51). In 1962, the Association for Computational Linguistics was founded in the United States, and the journal of the association, Computational Linguistics, was also published. It was roughly estimated that by 1965, there were eighteen countries or research institutions engaged in the studies on machine translation, including the United States, former Soviet Union, the United Kingdom, Japan, France, West Germany, Italy, former Czechoslovakia, former Yugoslavia, East Germany, Mexico, Hungary, Canada, Holland, Romania, and Belgium (Zhang 2006: 30−34).The development of machine translation in the United States since the late 1940s, however, fell short of expectations. In 1963, the Georgetown machine translation project was terminated, which signifies the end of the largest machine translation project in the United States (Chan 2004: 303). In 1964, the government of the United States set up the Automatic Language Processing Advisory Committee (ALPAC) comprising seven experts to enquire into the state of machine translation (ALPAC 1966; Warwick 1987: 22–37). In 1966, the report of the Committee, entitled Languages and Machines: Computers in Translation and Linguistics, pointed out that ‘there is no immediate or predictable prospect of useful machine translation’ (ALPAC 1966: 32). As machine translation was twice as expensive as human translation, it was unable to meet people’s expectations, and the Committee recommended that resources to support machine translation be terminated. Its report also mentioned that ‘as it becomes increasingly evident that fully automatic high-quality machine translation was not going to be realized for a long time, interest began to be shown in machine-aided translation’ (ibid.: 25). It added that machine translation should shift to machine-aided translation, which was ‘aimed at improved human translation, with an appropriate use of machine aids’ (ibid.: iii), and that ‘machine-aided translation may be an important avenue toward better, quicker, and cheaper translation’ (ibid.: 32). The ALPAC report dealt a serious blow to machine translation in the United States, which was to remain stagnant for more than a decade, and it also made a negative impact on the research on machine translation in Europe and Russia. But this gave an opportunity to machine-aided translation to come into being. All these events show that the birth of machine- aided translation is closely related to the development of machine translation. Computer-aided translation, nevertheless, would not be possible without the support of related concepts and software. It was no mere coincidence that translation memory, which is one of the major concepts and functions of computer-aided translation, came out during this period. According to W. John Hutchins, the concept of translation memory can be traced to the period from the 1960s to the 1980s (Hutchins 1998: 287−307). In 1978, when Alan Melby of the Translation Research Group of Brigham Young University conducted research on machine translation and developed an interactive translation system, ALPS (Automated Language Processing Systems), he incorporated the idea of translation memory into a tool called ‘Repetitions Processing’, which aimed at finding matched strings (Melby 1978; Melby and Warner 1995: 187). In the following year, Peter Arthern, in his paper on the issue of whether machine translation should be used in a conference organized by the European Commission, proposed the method of ‘translation by text-retrieval’ (Arthern 1979: 93). According to Arthern, This information would have to be stored in such a way that any given portion of text in any of the languages involved can be located immediately … together with its translation into any or all of the other languages which the organization employs. (Arthern 1979: 95) S. Chan Development of translation technology5 In October 1980, Martin Kay published an article, entitled ‘The Proper Place of Men and Machines in Language Translation’, at the Palo Alto Research Center of Xerox. He proposed to create a machine translation system in which the display on the screen is divided into two windows. The text to be translated appears in the upper window and the translation would be composed in the bottom one to allow the translator to edit the translation with the help of simple facilities peculiar to translation, such as aids for word selection and dictionary consultation, which are labelled by Kay as a translator amanuensis (Kay 1980: 9−18). In view of the level of word-processing capacities at that time, his proposal was inspiring to the development of computer-aided translation and exerted a huge impact on its research later on. Kay is generally considered a forerunner in proposing an interactive translation system. It can be seen that the idea of translation memory was established in the late 1970s and the 1980s. Hutchins believed that the first person to propose the concept of translation memory was Arthern. As Melby and Arthern proposed the idea almost at the same time, both could be considered as forerunners. And it should be acknowledged that Arthern, Melby, and Kay made a great contribution to the growth of computer-aided translation in its early days. The first attempt to deploy the idea of translation memory in a machine translation system was made by Alan Melby and his co-researchers at Brigham Young University, who jointly developed the Automated Language Processing Systems, or ALPS for short. This system provided access to previously translated segments which were identical (Hutchins 1998: 291). Some scholars classify this type of full match a function of the first generation translation memory systems (Gotti et al. 2005: 26−30; Kavak 2009; Elita and Gavrila 2006: 24−26). One of the major shortcomings of this generation of computer-aided translation systems is that sentences with full matching were very small in number, minimizing the reusability of translation memory and the role of translation memory database (Wang 2011: 141). Some researchers around 1980 began to collect and store translation samples with the intention of redeploying and sharing their translation resources. Constrained by the limitations of computer hardware (such as its limited storage space), the cost of building a bilingual database was high, and with the immaturity in the algorithms for bilingual data alignment, translation memory technology had been in a stage of exploration. As a result, a truly commercial computer-aided translation system did not emerge during the sixteen years of this period and translation technology failed to make an impact on translation practice and the translation industry. 1984–1992: A period of steady growth The eight years between 1984 and 1992 are a period of steady growth for computer-aided translation and for some developments to take place. Corporate operation began in 1984, system commercialization, in 1988, and regional expansion, in 1992. Company operation It was during this period that the first computer-aided translation companies, Trados in Germany and Star Group in Switzerland, were founded in 1984. These two companies later had a great impact on the development of computer-aided translation. The German company was founded by Jochen Hummel and Iko Knyphausen in Stuttgart, Germany, in 1984. Trados GmbH came from TRAnslation and DOcumentation Software. This company was set up initially as a language service provider (LSP) to work on a translation project they received from IBM in the same year. As the company later developed computer- 6 aided translation software to help complete the project, the establishment of Trados GmbH is regarded as the starting point of the period of steady growth in computer-aided translation (Garcia and Stevenson 2005: 18–31; http://www.lspzone.com).Of equal significance was the founding of the Swiss company STAR AG in the same year. STAR, an acronym of Software, Translation, Artwork, and Recording, provided manual technical editing and translation with information technology and automation. Two years later, STAR opened its first foreign office in Germany in order to serve the increasingly important software localization market and later developed STAR software products, GRIPS and Transit for information management and translation memory respectfully. At the same time, client demand and growing export markets led to the establishment of additional overseas locations in Japan and China. The STAR Group still plays an important role in the translation technology industry (http://www.star-group.net). It can be observed that during this early period of computer-aided translation, all companies in the field were either established or operated in Europe. This Eurocentric phenomenon was going to change in the next period. System commercialization The commercialization of computer-aided translation systems began in 1988, when Eiichiro Sumita and Yutaka Tsutsumi of the Japanese branch of IBM released the ETOC (Easy to Consult) tool, which was actually an upgraded electronic dictionary. Consultation of a traditional electronic dictionary was by individual words. It could not search phrases or sentences with more than two words. ETOC offered a flexible solution. When inputting a sentence to be searched into ETOC, the system would try to extract it from its dictionary. If no matches were found, the system would make a grammatical analysis of the sentence, taking away some substantive words but keeping the form words and adjectives which formed the sentence pattern. The sentence pattern would be compared with bilingual sentences in the dictionary database to find sentences with a similar pattern, which would be displayed for the translator to select. The translator could then copy and paste the sentence onto the Editor and revise the sentence to complete the translation. Though the system did not use the term translation memory and the translation database was still called a ‘dictionary’, it nevertheless had essentially the basic features of translation memory of today. The main shortcoming of this system is that as it needs to make grammatical analyses, its programming would be difficult and its scalability would be limited. If a new language were to be added, a grammatical analysis module would have to be programmed for the language. Furthermore, as the system could only work on perfect matching but not fuzzy matching, it drastically cut down on the reusability of translations (Sumita and Tsutsumi 1988: 2). In 1988, Trados developed TED, a plug-in for text processor tool that was later to become, in expanded form, the first Translator’s Workbench editor, developed by two people and their secretary (Garcia and Stevenson 2005: 18–31). It was around this time that Trados made the decision to split the company, passing the translation services part of the business to INK in the Netherlands, so that they could concentrate on developing translation software (http://www. translationzone.com). Two years later, the company also released the first version of MultiTerm as a memory- resident multilingual terminology management tool for DOS, taking the innovative approach of storing all data in a single, freely structured database with entries classified by user-defined attributes (Eurolux Computers 1992: 8; http://www.translationzone.com; Wassmer 2011). S. Chan Development of translation technology7 In 1991 STAR AG also released worldwide the Transit 1.0 (‘Transit’ was derived from the phrase ‘translate it’) 32-bit DOS version, which had been under development since 1987 and used exclusively for in-house production. Transit featured the modules that are standard features of today’s CAT systems, such as a proprietary translation editor with separate but synchronized windows for source and target language and tag protection, a translation memory engine, a terminology management component and project management features. In the context of system development, the ideas of terminology management and project management began with Transit 1.0. Additional products were later developed for the implementation and automation of corporate product communications: TermStar, WebTerm, GRIPS, MindReader, SPIDER and STAR James (http://www.star-group.net). One of the most important events in this period is obviously the release of the first commercial system, Trados, in 1992, which marks the beginning of commercial computer- aided translation systems. Regional expansion The year 1992 also marks the beginning of the regional expansion of computer-aided translation. This year witnessed some significant advances in translation software made in different countries. First, in Germany, Translator’s Workbench I and Translator’s Workbench II (DOS version of Trados) were launched within the year, with Workbench II being a standalone package with an integrated editor. Translator’s Workbench II comprises the TW II Editor (formally TED) and MultiTerm 2. Translator’s Workbench II was the first system to incorporate a ‘translation memory’ and alignment facilities into its workstation. Also of considerable significance was the creation by Matthias Heyn of Trados’s T Align, later known as WinAlign, the first alignment tool on the market. In addition, Trados began to open  a network of global offices, including Brussels, Virginia, the United Kingdom and Switzerland (Brace 1994; Eurolux Computers 1992; http://www.translationzone.com; Hutchins 1998: 287–307). Second, in the United States, IBM launched its IBM Translation Manager / 2 (TM/2), with an Operating System/2 (OS/2) package that integrated a variety of translation aids within a Presentation Manager interface. TM/2 had its own editor and a translation memory feature which used fuzzy search algorithms to retrieve existing material from its translation database. TM/2 could analyse texts to extract terms. TM/2 came with lemmatizers, spelling lists, and other linguistic resources for nineteen languages, including Catalan, Flemish, Norwegian, Portuguese, Greek, and Icelandic. External dictionaries could also be integrated into TM/2, provided they were formatted in Standard Generalized Markup Language (SGML). TM/2 could be linked to logic-based machine translation (Brace 1992a). This system is perhaps the first hybrid computer-aided translation system that was integrated with a machine translation system (Brace 1993; Wassmer 2011). Third, in Russia, the PROMT Ltd was founded by two doctorates in computational linguistics, Svetlana Sokolova and Alexander Serebryakov, in St. Petersburg in 1991. At the beginning, the company mainly developed machine translation (MT) technology, which has been at the heart of the @promt products. Later, it began to provide a full range of translation solutions: machine translation systems and services, dictionaries, translation memory systems, data mining systems (http://www.promt.com). Fourth, in the United Kingdom, two companies specializing in translation software production were founded. First, Mark Lancaster established the SDL International, which served as a service provider for the globalization of software (http://www.sdl.com). Second, 8 ATA Software Technology Ltd, a London-based software house specializing in Arabic translation software, was established in 1992 by some programmers and Arabic software specialists. The company later developed a series of machine translation products (Arabic and English) and MT and TM hybrid system, Xpro7 and online translation engine (http://www. atasoft.com). 1993–2003: A period of rapid growth This period, covering the years from 1993 to 2003, is a period of rapid growth, due largely to (1) the emergence of more commercial systems; (2) the development of more built-in functions; (3) the dominance of Windows operation systems; (4) the support of more document formats; (5) the support of more languages for translation; and (6) the dominance of Trados as a market leader. (1) The emergence of more commercial systems Before 1993, there were only three systems available on the market, including Translator’s Workbench II of Trados, IBM Translation Manager / 2, and STAR Transit 1.0. During this ten-year period between 1993 and 2003, about twenty systems were developed for sale, including the following better-known systems such as Déjà Vu, Eurolang Optimizer (Brace 1994), Wordfisher, SDLX, ForeignDesk, Trans Suite 2000, Yaxin CAT, Wordfast, Across, OmegaT, MultiTrans, Huajian, Heartsome, and Transwhiz. This means that there was a six- fold increase in commercial computer-aided translation systems during this period. Déjà Vu is the name of a computer-aided translation system developed by Atril in Spain after 1993. A preliminary version of Déjà Vu, a customizable computer-aided translation system that combined translation memory technology with example-based machine translation techniques, was initially developed by ATRIL in June to fulfil their own need for a professional translation tool. At first, they worked with machine translation systems, but the experiments with machine translation were extremely disappointing, and subsequent experiences with translation memory tools exposed two main shortcomings: all systems ran under MS-DOS and were capable of processing only plain text files. Then, ATRIL began considering the idea of writing its own translation memory software. Déjà Vu 1.0 was released to the public in November 1993. It was with an interface for Microsoft Word for Windows 2.0, which was defined as the first of its kind. Version 1.1 followed soon afterwards, incorporating several performance improvements and an integrated alignment tool (at a time when alignment tools were sold as expensive individual products), and setting a new standard for the translation tool market (http://www.atril.com). Déjà Vu, designed to be a professional translation tool, produced acceptable results at an affordable price. Déjà Vu was a first in many areas: the first TM tool for Windows; the first TM tool to directly integrate into Microsoft Word; the first 32-bit TM tool (Déjà Vu version 2.0); and the first affordable professional translation tool. In the following year, Eurolang Optimizer, a computer-aided translation system, was developed by Eurolang in France. Its components included the translator’s workstation, pre- translation server with translation memory and terminology database, and project management tool for multiple languages and users (Brace 1994). In Germany, Trados GmbH announced the release of the new Windows version of Translator’s Workbench, which could be used with standard Windows word processing packages via the Windows DDE interface (Brace 1994). In June 1994 Trados released S. Chan Development of translation technology9 MultiTerm Professional 1.5 which was included in Translator’s Workbench, which had fuzzy search to deliver successful searches even when words were incorrectly spelt, a dictionary-style interface, faster searches through use of new highly compressed data algorithms, drag and drop content into word processor and integrated programming language to create powerful layouts (http://www.translationzone.com). In Hungary, Tibor Környei developed the WordFisher for Microsoft Word macro set. The programme was written in the WordBasic language. For translators, it resembled a translation memory programme, but provided a simpler interface in Word (Környei 2000). In 1995, Nero AG was founded in Germany as a manufacturer of CD and DVD application software. Later, the company set up Across Systems GmbH as a division, which developed and marketed a tool of the same name for corporate translation management (CTM) that supported the project and workflow manage ment of translations (Schmidt 2006; German 2009: 9–10). During the first half of 1996, when Windows 95 was in its final stages of beta testing, Atril Development S.L. in Spain began writing a new version of Déjà Vu − not just porting the original code to 32 bits, but adding a large number of important functionalities that had been suggested by the users. In October, Atril released Déjà Vu beta v2.0. It consisted of the universal editor, Déjà Vu Interactive (DVI), the Database Maintenance module with an alignment tool, and a full-featured Terminology Maintenance module (Wassmer 2007: 37–38). In the same year, Déjà Vu again was the first TM tool available for 32-bit Windows and shipped with a number of filters for DTP packages − including FrameMaker, Interleaf, and QuarkXPress − and provided extensive project management facilities to enable project managers to handle large, multi-file, multilingual projects. In 1997, developments in France and Germany deserve mentioning. In France, CIMOS released Arabic to English translation software An-Nakel El-Arabi, with features like machine translation, customized dictionary and translation memory. Because of its deep sentence analysis and semantic connections, An-Nakel Al-Arabi could learn new rules and knowledge. CIMOS had previously released English to Arabic translation software (MultiLingual 1997). In Germany, Trados GmbH released WinAlign as a visual text alignment tool as the first fully- fledged 32-bit application in Trados. Mircosoft decided to base its internal localization memory store on Trados and consequently acquired a share of 20 per cent in Trados (http://www. translationzone.com). The year 1998 marks a milestone in the development of translation technology in China and Taiwan. In Beijing, Beijing Yaxincheng Software Technology Co. Ltd. 北京雅信誠公司 was set up as a developer of translation software. It was the first computer-aided translation software company in China. In Taipei, the Inventec Corporation released Dr Eye 98 (譯典通) with instant machine translation, dictionaries and termbases in Chinese and English (http://www. dreye.com.tw). In the same year, the activities of SDL and International Communications deserve special mention. In the United Kingdom, SDL began to acquire and develop translation and localization software and hardware − both for its own use in client-specific solutions, and to be sold as free-standing commercial products. At the end of the year, SDL also released SDLX, a suite of translation memory database tools. SDLX was developed and used in-house at SDL, and therefore was a mature product at its first offering (Hall 2000; MultiLingual 1998). Another British company, International Communications, a provider of localization, translation and multilingual communications services, released ForeignDesk v5.0 with the full support of Trados Translator’s Workbench 2.0 and WinAlign, S-Tagger. Then, Lionbridge Technologies Inc. acquired it (known as Massachusetts-based INT’L.com at the transaction) and later in November 2001 decided to open-source the ForeignDesk suite free of charge under BSD 10 licence. ForeignDesk was originally developed by International Communications around 1995 (MultiLingual 2000).In June 1999, Beijing YaxinCheng Software Technology Co. Ltd. established Shida CAT Research Centre (實達 CAT 研究中心), which later developed Yaxin CAT Bidirectional v2.5 (Chan 2004: 338). In June, SJTU Sunway Software Industry Ltd. acquired one of the most famous CAT products in China at the moment − Yaxin CAT from Beijing YaxinCheng Software Technology Co. Ltd., and it released the Yaxin CAT v1.0 in August. The release of this software signified, in a small way, that the development of computer-aided systems was no longer a European monopoly. In France, the first version of Wordfast PlusTools suite of CAT (Computer-Assisted Translation) tools was developed. One of the developers was Yves A. Champollion, who incorporated Wordfast LLC later. There were only a few TM software packages available in the first version. It could be downloaded freely before 2002, although registration was required (http://www.wordfast.net/champollion.net). In the United States, MultiCorpora R&D Inc. was incorporated, which was exclusively dedicated to providing language technology solutions to enterprises, governments, and language service providers (http://www.multicorpora.com). In the United Kingdom, following the launch of SDL International’s translation database tool, SDLX, SDL announced SDL Workbench. Packaged with SDLX, SDL Workbench memorized a user’s translations and automatically offered other possible translations and terminology from a user’s translation database within the Microsoft Word environment. In line with its ‘open’ design, it was able to work with a variety of file formats, including Trados and pre-translated RTF files (MultiLingual 1999). The year 2000 was a year of activities in the industry. In China, Yaxin CAT v2.5 Bidirectional (English and Chinese) was released with new features like seventy-four topic-specific lexicons with six million terms free of charge, project analysis, project management, share translation memory online and simultaneous editing of machine output (Chen 2001). In Germany, OmegaT, a free (GPL) translation memory tool, was publicly released. The key features of OmegaT were basic (the functionality was very limited), free, open-source, cross-operation systems as it was programmed in Java (http://www.omegat.org; Prior 2003). In Ireland, Alchemy Software Development Limited announced the acquisition of Corel CATALYST™, which was designed to boost the efficiency and quality of globalizing software products and was used by over 200 software development and globalization companies worldwide (http://www.alchemysoftware.ie). In the United Kingdom, SDL International announced in April the release of SDLX 2.0, which was a new and improved version of SDLX 1.03 (http://www.sdl.com). It also released SDL Webflow for managing multilingual website content (http://www.sdlintl.com). In Germany, Trados relocated its headquarters to the United States in March and became a Delaware corporation. In France, Wordfast v3.0 was released in September. The on-the-fly tagging and un-tagging of HTML (HyperText Markup Language) files was a major breakthrough in the industry. Freelance translators could translate HTML pages without worrying about the technical hurdles. Not much happened in 2001. In Taiwan, Inventec Corporation released Dr Eye 2001, with new functions like online search engine, full-text machine translation from English to Chinese, machine translation from Japanese to Chinese and localization plug-in (Xu 2001). In the United Kingdom, SDL International released SDLX 4.0 with real-time translation, a flexible software licence and enhanced capabilities. In the United States, Trados announced the launch of Trados 5 in two versions, Freelance and Team (http://www.translationzone.com). S. Chan Development of translation technology11 In contrast, the year 2002 was full of activities in the industry. In North America, MultiCorpora R&D Inc. in Canada released MultiTrans 3, providing corpus-based translation support and language management solution. It also introduced a new translation technology called Advanced Leveraging Translation Memory (ALTM). This model provided past translations in their original context and required virtually no alignment maintenance to obtain superior alignment results. In the United States, Trados 5.5 (Trados Corporate Translation Solution™) was released. MultiCorpora released MultiTrans 3.0, which introduced an optional client-server add-on, so it could be used in a web-based, multi-user environment or as a standalone workstation. Version 3 supported TMX and was also fully Unicode compliant (Locke and Giguère 2002: 51). In Europe and the United Kingdom, SDL International released its new SDLX Translation Suite 4, and then later that year released the elite version of the suite. The SDLX Translation Suite features a modular architecture consisting of five to eight components: SDL Project Wizard, SDL Align, SDL Maintain, SDL Edit and SDL TermBase in all versions, and SDL Analyse, SDL Apply and SDLX AutoTrans in the Professional and Elite versions (Wassmer 2003). In Germany, MetaTexis Software and Services released in April the first official version 1.00 of MetaTexis (http://www.metatexis.com). In Asia, Huajian Corporation in China released Huajian IAT, a computer-aided translation system (http://www.hjtek.com). In Taiwan, Otek launched in July Transwhiz Power version (client/server structure), which aimed at enterprise customers (http://www.otek.com.tw). In Singapore, Heartsome Holdings Pte. Ltd. was founded to develop language translation technology (Garcia and Stevenson 2006: 77). North America and Europe were active in translation technology in 2003. In 2003, MultiCorpora R&D Inc. in Canada released MultiTrans 3.5 which had new and improved capabilities, including increased processing speed of automated searches, increased network communications speed, improved automatic text alignment for all languages, and optional corpus-based pre-translation. Version 3.5 also offered several new terminology management features, such as support for additional data types, additional filters, batch updates and added import and export flexibility, as well as full Microsoft Office 2003 compatibility, enhanced Web security and document analysis capabilities for a wider variety of document formats (MultiLingual 2003). In the United States, Trados 6 was launched in April and Trados 6.5 was launched in October with new features like auto concordance search, Word 2003 support and access to internet TM server (Wassmer 2004: 61). In Germany, MetaTexis version 2.0 was released in October with a new database engine. And MetaTexis version ‘Net/Office’ was released with new features that supported Microsoft PowerPoint and Excel files, Trados Workbench, and could be connected with Logoport servers (http://www.metatexis.com). In Russia, PROMT, a developer of machine translation products and services, released a new version @promt XT with new functions like processing PDF file formats, which made PROMT the first among translation software that supported PDF. Also, one of the editions, @promt Expert integrated translation memory solutions (Trados) and a proprietary terminology extraction system (http://www.promt.com). In France, Atril, which was originally founded in Spain but which relocated its group business to France in the late 1990s, released Déjà Vu X (Standard, Professional, Workgroup and Term Sever) (Harmsen 2008). Wordfast 4, which could import and translate PDF contents, was also released (http://www.wordfast.net). Some developers of machine translation systems also launched new versions with a translation memory component, such as LogoVista, An-Nabel El-Arabi and PROMT (http://www. 12 promt.com). Each of these systems was created with distinct philosophies in its design, offering its own solutions to problems and issues in the work of translation. This was aptly pointed out by Brace (1994):Eurolang Optimizer is based on an ambitious client / server architecture designed primarily for the management of large translation jobs. Trados Workbench, on the other hand, offers more refined linguistic analysis and has been carefully engineered to increase the productivity of single translators and small workgroups. (2) The development of more built-in functions Computer-aided translation systems of the first and second periods were usually equipped with basic components, such as translation memory, terminology management, and translation editor. In this period, more functions were developed and more components were gradually integrated into computer-aided translation systems. Of all the new functions developed, tools for alignment, machine translation, and project management were most significant. Trados Translator’s Workbench II, for example, incorporated T Align, later known as WinAlign, into its workstation, followed by other systems such as Déjà Vu, SDLX, Wordfisher, and MultiTrans. Machine translation was also integrated into computer-aided translation systems to handle segments not found in translation memories. IBM’s Translation Manager, for example, introduced its Logic-Based Machine Translation (LMT) to run on IBM mainframes and RS/6000 Unix systems (Brace 1993). The function of project management was also introduced by Eurolang Optimizer in 1994 to better manage translation memory and terminology databases for multiple languages and users (Brace 1992a). (3) The dominance of Windows Operating System Computer-aided translation systems created before 1993 were run either in the DOS system or OS/2 system. In 1993, the Windows versions of these systems were first introduced and they later became the dominant stream. For example, IBM and Trados GmbH released a Windows version of TM/2 and of Translator’s Workbench respectively in mid-1993. More Windows versions came onto the market, such as the preliminary version of ATRIL’s Déjà Vu 1.0 in June in Spain. Other newly released systems running on Windows include SDLX, ForeignDesk, Trans Suite 2000, Yaxin CAT, Across, MultiTrans, Huajian, and TransWhiz. (4) The support of more document formats Computer-aided translation systems of this period could handle more document formats directly or with filters, including Adobe InDesign, FrameMaker, HTML, Microsoft PowerPoint, Excel, Word, QuarkXPress, even PDF by 2003. Trados 6.5, for example, supported all the widely used file formats in the translation community, which allowed translators and translation companies to translate documents in Microsoft Office 2003 Word, Excel and PowerPoint, Adobe InDesign 2.0, FrameMaker 7.0, QuarkXPress 5, and PageMaker. (5) The support of translation of more languages Translation memory is supposed to be language-independent, but computer-aided translation systems developed in the early 1990s did not support all languages. In 1992, Translator S. Chan Development of translation technology13 Workbench Editor, for example, supported only five European languages, namely, German, English, French, Italian and Spanish, while IBM Translation Manager / 2 supported 19 languages, including Chinese, Korean and other OS/2 compatible character code sets. This was due largely to the contribution of Unicode, which provided the basis for the processing, storage, and interchange of text data in any language in all modern software, thereby allowing developers of computer-aided translation systems to gradually resolve obstacles in language processing, especially after the release of Microsoft Office 2000. Systems with Unicode support mushroomed, including Transit 3.0 in 1999, MultiTerm and WordFisher 4.2.0 in 2000, Wordfast Classic 3.34 in 2001, and Tr-AID 2.0 and MultiTrans 3 in 2002. (6) The dominance of Trados as a market leader As a forerunner in the field, Trados became a market leader in this period. As observed by Colin Brace, ‘Trados has built up a solid technological base and a good market position’ in its first decade. By 1994, the company had a range of translation software, including Trados Translator’s Workbench (Windows and DOS versions), MultiTerm Pro, MultiTerm Lite, and MultiTerm Dictionary. Its technology in translation memory and file format was then widely used in other computer-aided translation systems and its products were most popular in the industry. From the late 1990s, a few systems began to integrate Trados’s translation memory into their systems. In 1997, ProMemoria, for example, was launched with its translation memory component provided by Trados. In 1998, International Communications released ForeignDesk 5.0 with the full support of Trados Translator’s Workbench 2.0, WinAlign, and S-Tagger. In 1999, SDLX supported import and export formats such as Trados and tab- delimited and CSV files. In 2000, Trans Suite 2000 was released with the capacity to process Trados RTF file. In 2001, Wordfast 3.22 could directly open Trados TMW translation memories (Translator’s Workbench versions 2 and 3). In 2003, PROMT XT Export integrated Trados’s translation memory. In October 2003, MetaTexis ‘Net/Office’ 2.0 was released and was able to work with Trados Workbench. 2004–2013: A period of global development Advances in technology have given added capabilities to computer-aided translation systems. During the last nine years, while most old systems have been upgraded on a regular basis, close to thirty new systems have been released to the market. This situation has offered a wider range of choices for buyers to acquire systems with different packages, functions, operation systems, and prices. One of the most significant changes in this period is the addition of new computer-aided translation companies in countries other than those mentioned above. Hungary is a typical example. In 2004, Kilgray Translation Technologies was established by three Hungarian language technologists. The name of the company was made up of the founders’ surnames: Kis Balázs (KI), Lengyel István (L), and Ugray Gábor (GRAY). Later, the company launched the first version of MemoQ, an integrated Localization Environment (ILE), in 2005. MemoQ’s first version had a server component that enabled the creation of server projects. Products of Kilgray included MemoQ, MemoQ server, QTerm, and TM Repository (http://www.kilgray.com). Another example is Japan. In Japan, Rozetta Corporation released TraTool, a computer- aided translation system with translation memory, an integrated alignment tool, an integrated terminology tool and a user dictionary. The product is still commercially available but no major improvement has been made since its first version (http://www.tratool.com). 14 Yet another example is Poland, where AidTrans Soft launched its AidTrans Studio 1.00, a translation memory tool. But the company was discontinued in 2010 (http://www. thelanguagedirectory.com/translation/translation_software). New versions of computer-aided translation systems with new features are worth noting. In the United Kingdom, ATA launched a new Arabic Memory Translation system, Xpro7 which had the function of machine translation (http://www.atasoft.com). SDL Desktop Products, a division of SDL International, announced the launch of SDLX 2004. Its new features included TMX Certification, seamlessly integrating with Enterprise systems such as online terminology and multilingual workflow management, adaptation of new file formats, synchronized web- enabled TM, and Knowledge-based Translation (http://www.sdl.com). In the United States, Systran released Systran Professional Premium 5.0, which contained integrated tools such as integrated translation memory with TMX support, a Translator’s Workbench for post-editing and ongoing quality analysis (http://www.systransoft.com). Multilizer Inc., a developer of globalization technologies in the United States, released a new version of Multilizer, which included multi-user translation memory with Translation Memory Manager (TMM), a standalone tool for maintaining Multilizer Translation Memory contents. TMM allowed editing, adding and deleting translations, and also included a briefcase model for working with translations off-line (http://www.multilizer.com). In Ukraine, Advanced International Translations (AIT) started work on user-friendly translation memory software, later known as AnyMen, which was released in December 2008. In 2005, translation technology moved further ahead with new versions and new functions. In North America, MultiCorpora in Canada released MultiTrans 4, which built on the foundation of MultiTrans 3.7 and had a new alignment process that was completely automated (MultiLingual 2005d). Trados, incorporated in the United States, produced Trados 7 Freelance, which supported twenty additional languages, including Hindi. At an operating system level, Microsoft Windows 2000, Windows XP Home, Windows XP Professional, and Windows 2003 Server were supported. More file formats were now directly supported by TagEditor. MultiCorpora also introduced MultiTrans 4, which was designed to meet the needs of large organizations by providing the newest efficiencies for translators in the areas of text alignment quality, user-friendliness, flexibility and web access (http://www.multicorpora.com). In Europe, Lingua et Machina, a memory translation tool developer, released SIMILIS v1.4, its second-generation translation tool. SIMILIS uses linguistic parsers in conjunction with the translation memory paradigm. This function allowed for the automatic extraction of bilingual terminology from translated documents. Version 1.4 brought compatibility with the Trados translation memory format (Text and TMX) and a new language, German (MultiLingual 2005b). In Switzerland, STAR Group released Transit XV Service Pack 14. This version extended its capabilities with a number of new features and support of 160 languages and language versions, including Urdu (India) and Urdu (Pakistan). It supported Microsoft Word 2003 files and had MySpell dictionaries (MultiLingual 2005a). PROMT released @promt 7.0 translation software, which supported the integrated translation memory, the first of its kind among PROMT’s products (http://www.promt.com). In the United Kingdom, SDL Desktop Products released the latest version of its translation memory tool SDLX 2005, which expanded the Terminology QA Check and automatically checked source and translations for inconsistent, incomplete, partial or empty translations, corrupt characters, and consistent regular expressions, punctuation, and formatting. Language support had been added for Maltese, Armenian and Georgian, and the system could handle more than 150 languages (MultiLingual 2005c). In June, SDL International acquired Trados for £35 million. The acquisition provided extensive end-to-end technology and service S. Chan Development of translation technology15 solutions for global information assets (http://www.translationzone.com). In October, SDL Synergy was released as a new project management tool on the market.In Asia, Huajian Corporation in China released in June Huajian Multilingual IAT network version (華建多語 IAT 網絡版) and in October Huajian IAT (Russian to Chinese) standalone version (http://www.hjtrans.com). In July, Beijing Orient Yaxin Software Technology Co. Ltd. released Yaxin CAT 2.0, which was a suite including Yaxin CAT 3.5, CAM 3.5, Server, Lexicons, Translation Memory Maintenance and Example Base. In Singapore, Heartsome Holdings Pte. Ltd. released Heartsome Translation Suite, which was composed of three programs: an XLIFF Editor in which source files were converted to XLIFF format and translated; a TMX Editor that dealt with TMX files; and a Dictionary Editor that dealt with TBX files (Garcia and Stevenson 2006: 77). In Taiwan, Otek released Transwhiz 9.0 for English, Chinese and Japanese languages (http://www.otek.com.tw). Significant advances in translation technology were made in 2006 particularly in Europe, the United Kingdom, and the United States. In Europe, Across Systems GmbH in Germany released in September its Corporate Translation Management 3.5, which marked the start of the worldwide rollout of Across software (MultiLingual 2006a). In the United Kingdom, SDL International released in February SDL Trados 2006, which integrated with Translators Workbench, TagEditor, SDLX editing environments and SDL MultiTerm. It included new support for Quark, InDesign CS2 and Java (http://www.sdl.com). In the United States, MultiCorpora launched TextBase TM concept (http://www.multicorpora.com). Apple Inc. released in August AppleTrans, a text editor specially designed for translators, featuring online corpora which represented ‘translation memory’ accessible through documents. AppleTrans helped users localize web pages (http:// developer.apple.com). Lingotek, a language search engine developer in the United States, launched a beta version of a collaborative language translation service that enhanced a translator’s efficiency by quickly finding meaning-based translated material for re-use. Lingotek’s language search engine indexed linguistic knowledge from a growing repository of multilingual content and language translations, instead of web pages. Users could then access its database of previously translated material to find more specific combinations of words for re-use. Such meaning-based searching maintained better style, tone, and terminology. Lingotek ran completely within most popular web browsers, including initial support for Internet Explorer and Firefox. Lingotek supported Word, Rich Text Format (RTF), Open Office, HTML, XHTML and Excel formats, thereby allowing users to upload such documents directly into Lingotek. Lingotek also supported existing translation memory files that were TMX- compliant memories, thus allowing users to import TMX files into both private and public indices (MultiLingual 2006b). In 2007, Wordfast 5.5 was released in France. It was an upgrade from Wordfast 4, in which Mac support was completely overhauled. This version continued to offer translators collaboration community via a LAN. Each Wordfast licence granted users the ability to search Wordfast’s web-based TM and knowledge base, VLTM (http://www.wordfast.net). In Germany, a group of independent translators and programmers under the GNU GPL licence developed in October Anaphraseus, a computer-aided translation tool for creating, managing and using bilingual translation memories. Originally, Anaphraseus was developed to work with the Wordfast TM format, but it could also export and import files in TMX format (http:// anaphraseus.sourceforge.net). In Hungary, Kilgray Translation Technologies released in January MemoQ 2.0. The main theme for the new version was networking, featuring a new resource server. This server not only stored translation memory and term bases, but also offered the possibility of creating server projects that allowed for the easy distribution of work among 16 several translators and ensured productivity at an early stage of the learning curve. Improvements on the client side included support for XML and Adobe FrameMaker MIF file formats; improvements to all other supported file formats; and support for the Segmentation Rule eXchange standard, auto-propagation of translated segments, better navigation and over a hundred more minor enhancements (Multilingual 2007). In Russia, MT2007, a freeware, was developed by a professional programmer Andrew Manson. The main idea was to develop easy- to-use software with extensive features. This software lacked many features that leading systems had. In the United Kingdom, SDL International released in March SDL Trados 2007, which had features such as a new concept of project delivery and supply chain, new one-central-view dashboard for new project wizard, PerfectMatch, automated quality assurance checker and full support for Microsoft Office 2007 and Windows Vista.In the United States, MultiCorpora’s Advanced Leveraging launched WordAlign which boasted the ability to align text at the individual term and expression level (http://www. multicorpora.com). MadCap Software Inc., a multi-channel content authoring company, developed in May MadCap Lingo, an XML-based, fully-integrated Help authoring tool and translation environment. MadCap Lingo offered an easy-to-use interface, complete Unicode support for all left-to-right languages for assisting localization tasks. Across Systems GmbH and MadCap Software announced a partnership to combine technical content creation with advanced translation and localization. In June, Alchemy Software Development Ltd. and MadCap Software, Inc. announced a joint technology partnership that combined technical content creation with visual TM technology. In 2008, Europe again figured prominently in computer-aided translation software production. In Germany, Across Systems GmbH released in April Across Language Server 4.0 Service Pack 1, which contained various extensions in addition to authoring, such as FrameMaker 8 and SGML support, context matching, and improvements for web-based translations via crossWeb (MultiLingual 2008a). It also introduced in July its new Language Portal Solution (later known as Across Language Portal) for large-scale organizations and multinational corporations, which allowed customers operating on an international scale to implement Web portals for all language-related issues and for all staff levels that need to make use of language resources. At the same time Across released the latest update to the Across Language Server, offering many new functions for the localization of software user interfaces (http://www.across.net). In Luxembourg, Wordbee S.A. was founded as a translation software company focusing on web-based integrated CAT and management solutions (http://www. wordbee.com). In Eastern Europe, Kilgray Translation Technologies in Hungary released in September MemoQ 3.0, which included a new termbase and provided new terminology features. It introduced full support for XLIFF as a bilingual format and offered the visual localization of RESX files. MemoQ 3.0 was available in English, German, Japanese and Hungarian (http:// kilgray.com). In Russia, Promt released in March 8.0 version with major improvement in its translation engine, translation memory system with TMX files import support, and dialect support in English (UK and American), Spanish (Castilian and Latin American), Portuguese (Portuguese and Brazilian), German (German and Swiss) and French (French, Swiss, Belgian, Canadian) (http://www.promt.com). In Ukraine, Advanced International Translations (AIT) released in December AnyMen, a translation memory system compatible with Microsoft Word. In Uruguay, Maxprograms launched in April Swordfish version 1.0-0, a cross-platform computer-aided translation tool based on the XLIFF 1.2 open standard published by OASIS (http://www.maxprograms.com). In November, this company released Stingray version 1.0-0, a cross-platform document aligner. The translation memories in TMX, CSV or Trados S. Chan Development of translation technology17 TXT format generated by Stingray could be used in most modern computer-aided translation systems (http://www.maxprograms.com).In Ireland, Alchemy Software Development, a company in visual localization solutions, released in July Alchemy PUBLISHER 2.0, which combined visual localization technology with translation memory for documentation. It supported standard documentation formats, such as MS Word, XML, application platforms such as Windows 16/22/64x binaries, web- contents formats such as HTML, ASP, and all derivative content formats (http://www. alchemysoftware.ie). In North America, JiveFusion Technologies, Inc. in Canada officially launched Fusion One and Fusion Collaborate 3.0. The launches introduced a new method of managing translation memories. New features included complete contextual referencing. JiveFusion also integrated Fusion Collaborate 3.0 with TransFlow, a project and workflow management solution by Logosoft (MultiLingual 2008b). In the United States, MadCap Software, Inc. released in February MadCap Lingo 2.0, which included the Darwin Information Typing Architecture standard, Microsoft Word and a range of standard text and language formats. In September, it released MadCap Lingo 3.0, which included a new project packager function designed to bridge the gap between authors and translators who used other translation memory system software and a new TermBase Editor for creating databases of reusable translated terms. In Asia, Yaxin CAT 4.0 was released in China in August with some new features including a computer-aided project platform for project management and huge databases for handling large translation projects. In Taiwan, Otek released Transwhiz 10 for translating English, Chinese and Japanese languages, with fuzzy search engine and Microsoft Word workstation (http://www.otek.com.tw). The year 2009 witnessed the development of Autshumato Integrated Translation Environment (ITE) version 1.0, a project funded by the Department of Arts and Culture of the Republic of South Africa. It was released by The Centre for Text Technology (CTexT®) at the Potchefstroom Campus of the North-West University and University of Pretoria after two years of research and development. Although Autshumato ITE was specifically developed for the eleven official South African languages, it was in essence language independent, and could be adapted for translating between any language pair. In Europe, Wordfast released in January Wordfast Translation Studio, a bundled product with Wordfast Classic (for Microsoft Word) and Wordfast Pro (a standalone CAT platform). With over 15,000 licences in active use, Wordfast claimed itself the second most widely used translation memory tool (http://www.wordfast.net). In Germany, Across Systems GmbH released in May Across Language Server 5.0, which offered several options for process automation as well as for workflow management and analysis. Approximately fifty connections were available for interacting with other systems (MultiLingual 2009b). In September, STAR Group in Switzerland released Transit NXT (Professional, Freelance Pro, Workstation, and Freelance). Service pack 1 for Transit NXT/TermStar NXT contained additional user interface languages for Chinese, Spanish, Japanese, and Khmer, enhanced alignment usability, support for QuarkXpress 7, and proofreading for internal repetitions. In the United Kingdom, SDL announced in June the launch of SDL Trados® Studio 2009 in the same month, which included the latest versions of SDL MultiTerm, SDL Passolo Essential, SDL Trados WinAlign, and SDL Trados 2007 Suite. New features included Context Match, AutoSuggest, QuickPlace (http://www.sdl.com). In October, SDL released its enterprise platform SDL TM Server™ 2009, a new solution to centralize, share, and control translation memories (http://www.sdl.com). 18 In North America, JiveFusion Technologies Inc. in Canada released in March Fusion 3.1 to enhance current TMX compatibility and the capability to import and export to TMX while preserving the complete segment context (MultiLingual 2009a). In the United States, Lingotek introduced software-as-a-service collaborative translation technology which combined the workflow and computer-aided translation capabilities of human and machine translation into one application. Organizations could upload new projects, assign translators (paid or unpaid), check the status of current projects in real time and download completed documents from any computer with web access (MultiLingual 2009c). In Asia, Beijing Zhongke LongRay Software and Technology Ltd. Co. in China released in September LongRay CAT 3.0 (standalone edition), a CAT system with translation memory, alignment, dictionary and terminology management and other functions (http://www.zklr. com). In November, Foshan Snowman Computer Co. Ltd. released Snowman version 1.0 in China (http://www.gcys.cn). Snowman deserves some mentioning because (1) it was new; (2) the green trial version of Snowman could be downloaded free of charge; (3) it was easy to use as its interface was user-friendly and the system was easy to operate; and (4) it had the language pair of Chinese and English, which caters to the huge domestic market as well as the market abroad. Most of the activities relating to computer-aided translation in 2010 took place in Europe and North America. In Germany, Across Systems GmbH released in August Across Language Server v. 5 Service Pack 1, which introduced a series of new functionalities and modes of operation relating to the areas of project management, machine translation, crowdsourcing and authoring assistance (http://new.multilingual.com). In October, MetaTexis version 3.0 was released, which imported filter for Wordfast Pro and Trados Studio translation memories and documents (http://www.metatexis.com). In France, Wordfast LLC released in July Wordfast Pro 2.4 (WFP) with over sixty enhancements. This system was a standalone environment that featured a highly customizable interface, enhanced batch processing functionality, and increased file format support (http://www.wordfast.net). In October, Wordfast LLC created an application to support translation on the iPhone and iPad in the Wordfast Anywhere environment (http:// www.wordfast.net). In Hungary, Kilgray Translation Technologies released in February MemoQ 4.0, which was integrated with project management functions for project managers who wanted to have more control and enable translators to work in any translation tool. In October, the company released MemoQ 4.5, which had a rewritten translation memory engine and improvements to the alignment algorithm (http://www.kilgray.com). In France, Atril released in March TeaM Server, which allowed translators with Déjà Vu Workgroup to work on multinational and multisite translation projects on a LAN or over the Internet, sharing their translations in real-time, ensuring superior quality and consistency. TeaM Server also provided scalable centralized storage for translation memories and terminology databases. The size of translation repositories and the number of concurrent users were only limited by the server hardware and bandwidth (http://www.atril.com). In October, Atril released Déjà Vu X2 in four editions: Editor, Standard, Professional, and Workgroup. Its new features included DeepMiner data extraction engine, new StartView interface, and AutoWrite word prediction. In Switzerland, STAR Group released in October Transit NXT Service Pack 3 and TermStar NXT. Transit NXT Service Pack 3 contained the following improvements: support of Microsoft Office 2007, InDesign CS5, QuarkXpress 8 and QuarkXpress 8.1, and PDF synchronization for MS Word files. In the United Kingdom, SDL released in March a new subscription level of its SDL Trados Studio, which included additional productivity tools for translators such as Service Pack 2, enabling translators to plug in to multiple automatic translation tools. The company also did a S. Chan Development of translation technology19 beta launch of SDL OpenExchange, inviting the developer community to make use of standard open application programming interfaces to increase the functionality of SDL Trados Studio (Multilingual 2010a). In September, XTM International released XTM Cloud, which was a totally online Software-as-a-Service (SaaS) computer-assisted translation tool set, combining translation workflow with translation memory, terminology management and a fully featured translator workbench. The launch of XTM Cloud means independent freelance translators have access to XTM for the first time (http://www.xtm-intl.com). In Ireland, Alchemy Software Development Limited released in May Alchemy PUBLISHER 3.0, which supports all aspects of the localization workflow, including form translation, engineering, testing, and project management. It also provided a Machine Translation connector which was jointly developed by PROMT, so that documentation formats could be machine translated (http:// www.alchemysoftware.ie; http://www.promt.com). In North America, IBM in the United States released in June the open source version of OpenTM/2, which originated from the IBM Translation Manager. OpenTM/2 integrated with several aspects of the end-to-end translation workflow (http://www.opentm2.org). Partnering with LISA (Localization Industry Standards Association), Welocalize, Cisco, and Linux Solution Group e.V. (LiSoG), IBM aimed to create an open source project that provided a full-featured, enterprise-level translation workbench environment for professional translators on OpenTM/2 project. According to LISA, OpenTM/2 not only provided a public and open implementation of translation workbench environment that served as the reference implementation of existing localization industry standards, such as TMX, it also aimed to provide standardized access to globalization process management software (http://www.lisa. org; LISA 2010). Lingotek upgraded in July its Collaborative Translation Platform (CTP) to a software-as-a-service product which combined machine translation, real-time community translation, and management tools (MultiLingual 2010b). MadCap Software, Inc. released in September MadCap Lingo v4.0, which had a new utility for easier translation alignment and a redesigned translation editor. Systran introduced in December Desktop 7 Product Suite, which included the Premium Translator, Business Translator, Office Translator, and Home Translator. Among them, Premium Translator and Business Translator were equipped with translation memory and project management features. In South America, Maxprograms in Uruguay released in April Swordfish II, which incorporated Anchovy version 1.0-0 as glossary manager and term extraction tool, and added support for SLD XLIFF files from Trados Studio 2009 and Microsoft Visio XML Drawings, etc. (http://www.maxprograms.com). In 2011, computer-aided translation was active in Europe and America. In Europe, ATRIL / PowerLing in France released in May Déjà Vu X2, a new version of its computer-assisted translation system, which had new features such as DeepMiner data mining and translation engine, SmartView Interface and a multi-file and multi-format alignment tool (MultiLingual 2011). In June, Wordfast Classic v6.0 was released with features such as the ability to share TMs and glossaries with an unlimited number of users, improved quality assurance, AutoComplete, and improved support for Microsoft Word 2007/2010 and Mac Word 2011 (http://www.wordfast.net). In Luxembourg, the Directorate-General for Translation of the European Commission released in January its one million segments of multilingual Translation Memory in TMX format in 231 language pairs. Translation units were extracted from one of its large shared translation memories in Euramis (European Advanced Multilingual Information System). This memory contained most, but not all, of the documents of the Acquis Communautaire, the entire body of European legislation, plus some other documents which were not part of the Acquis. In Switzerland, the STAR Group released 20 in February Service Pack 4 for Transit NXT and TermStar NXT. Transit NXT Service Pack 4 contained the following improvements: support of MS Office 2010, support of Quicksilver 3.5l, and preview for MS Office formats. In Eastern Europe, Kilgray Translation Technologies in Hungary released in June TM Repository, the world’s first tool-independent Translation Memory management system (http://kilgray.com). Kilgray Translation Technologies later released MemoQ v 5.0 with the AuditTrail concept to the workflow, which added new improvements like versioning, tracking changes (to show the difference of two versions), X-translate (to show changes on source texts), the Post Translation Analysis on formatting tags (Kilgray Translation Technologies 2011). In the United Kingdom, XTM International released in March XTM 5.5, providing both Cloud and On-Premise versions, which contained customizable workflows, a new search and replace feature in Translation Memory Manager and the redesign of XTM Workbench (http:// www.xtm-intl.com). In North America, MultiCorpora R&D Inc. in Canada released in May MutliTrans Prism, a translation management system (TMS) for project management, translation memory and terminology management (MultiCorpora 2011). In 2012, the development of computer-aided translation in various places was considerable and translation technology continued its march to globalization. In North America, the development of computer-aided translation was fast. In Canada, MultiCorpora, a provider of multilingual asset management solutions, released in June MultiTrans Prism version 5.5. The new version features a web editing server that extends control of the management of translation processes, and it can be fully integrated with content management systems. In September, Terminotix launched LogiTerm 5.2. Its upgrades and new features, including indexing TMX files directly in Bitext database, reinforced the fuzzy match window, and adjusted buttons (http://terminotix.com/news/newsletter). In December, MultiCorpora added new machine translation integrations to its MultiTrans Prism. The integration options include Systran, Google and Microsoft (http://www.multicorpora.com). In Asia, there was considerable progress in computer-aided translation in China. Transn Information Technology Co., Ltd. released TCAT 2.0 as freeware early in the year. New features of this software include the Translation Assistant (翻譯助理) placed at the sidebar of Microsoft Office, pre-translation with TM and termbase, source segment selection by highlighting (自動取句) (http://www.transn.com). In May, Foshan Snowman Computer Co. Ltd. released Snowman 1.27 and Snowman Collaborative Translation Platform (雪人 CAT 協 同翻譯平臺) free version. The platform offers a server for a central translation memory and termbase so that all the users can share their translations and terms, and the reviewers can view the translations simultaneously with translators. It also supports online instant communication, document management and online forum (BBS) (http://www.gcys.cn). In July, Chengdu Urelite Tech Co. Ltd. (成都優譯信息技術有限公司), which was founded in 2009, released Transmate, including the standalone edition (beta), internet edition and project management system. The standalone edition was freely available for download from the company’s website. The standalone edition of Transmate is targeted at freelancers and this beta release offers basic CAT functions, such as using TM and terminology during translation. It has features such as pre-translation, creating file-based translation memory, bilingual text export and links to an online dictionary website and Google MT (http://www.urelitetech.com.cn). Heartsome Translation Studio 8.0 was released by the Shenzhen Office of Heartsome in China. Its new features include pre-saving MT results and external proofreading file export in RTF format. The new and integrated interface also allows the user to work in a single unified environment in the translation process (http://www.heartsome.net). S. Chan Development of translation technology21 In Japan, Ryan Ginstrom developed and released Align Assist 1.5, which is freeware to align source and translation files to create translation memory. The main improvement of this version is the ability to set the format of a cell text (http://felix-cat.com). In October, LogoVista Corporation released LogoVista PRO 2013. It can support Microsoft Office 2010 64-bit and Windows 8. More Japanese and English words are included and the total number of words in dictionaries is 6.47 million (http://www.logovista.co.jp). In Europe, the developments of computer-aided translation systems are noteworthy. In the Czech Republic, the MemSource Technologies released in January MemSource Editor for translators as a free tool to work with MemSource Cloud and MemSource Server. The Editor is multiplatform and can be currently installed on Windows and Macintosh (http:// www.memsource.com). In April, this company released MemSource Cloud 2.0. MemSource Plugin, the former CAT component for Microsoft Word, is replaced by the new MemSource Editor, a standalone translation editor. Other new features include adding comments to segments, version control, translation workflow (only in the Team edition), better quality assurance and segmentation (http://blog.memsource.com). In December, MemSource Technologies released MemSource Cloud 2.8. It now encrypts all communication by default. This release also includes redesigned menu and tools. Based on the data about previous jobs, MemSource can suggest relevant linguistics for translation jobs (http://www.memsource.com). In France, Wordfast LLC released Wordfast Pro 3.0 in April. Its new features include bilingual review, batch TransCheck, filter 100 per cent matches, split and merge TXML files, reverse source/target and pseudo-translation (http://www.wordfast.com). In June Atril and PowerLing updated Déjà Vu X2. Its new features include an incorporated PDF converter and a CodeZapper Macro (http://www.atril.com). In Germany, Across Language Server v 5.5 was released in November. New features such as linguistic supply chain management are designed to make project and resources planning more transparent. The new version also supports the translation of display texts in various formats, and allows the protection of the translation units to ensure uniform use (http://www.across.net). In Hungary, Kilgray Translation Technologies released in July MemoQ 6.0 with new features like predictive typing and several new online workflow concepts such as FirstAccept (assign job to the first translator who accepted it on the online workflow), GroupSourcing, Slicing, and Subvendor group (http://kilgray.com). In December, the company released MemoQ 6.2. Its new features include SDL package support, InDesign support with preview, new quality assurance checks and the ability to work with multiple machine translation engines at the same time (http://kilgray.com). In Luxembourg, Wordbee in October designed a new business analysis module for its Wordbee translation management system, which provides a new dashboard where over 100 real-time reports are generated for every aspect of the localization process (http://www.wordbee.com). In Switzerland, STAR Group released Service Pack 6 for Transit NXT and TermStar NXT . The improvements of Service Pack 6 of Transit NXT contain the support of Windows 8 and Windows Server 2012, QuarkXPress 9.0-9.2, InDesign CS6, integrated OpenOffice spell check dictionaries, 10 additional Indian languages (http://www.star-group.net). In the United Kingdom, XTM International, a developer of XML authoring and translation tools, released in April XTM Suite 6.2. Its updates include a full integration with machine translation system, Asia Online Language Studio and the content management system XTRF. In October, the company released XTM Suite 7.0 and a new XTM Xchange module in XTM Cloud intended to increase the supply chain. Version 7.0 includes project management enhancements, allowing users to group files, assign translators to specific groups or languages, and create different workflows for different languages (http://www.xtm-intl.com). 22 During this period, the following trends are of note. 1 The systematic compatibility with Windows and Microsoft Office Of the sixty-seven currently available systems on the market, only one does not run on the Windows operation systems. Computer-aided translation systems have to keep up with the advances in Windows and Microsoft Office for the sake of compatibility. Wordfast 5.51j, for example, was released in April 2007, three months after the release of Windows Vista, and Wordfast 5.90v was released in July 2010 to support Microsoft Office Word 2007 and 2010. 2 The integration of workflow control into CAT systems Besides re-using or recycling translations of repetitive texts and text-based terminology, systems developed during this period added functions such as project management, spell check, quality assurance, and content control. Take SDL Trados Studio 2011 as an example. This version, which was released in September 2011, has a spell checking function for a larger number of languages and PerfectMatch 2.0 to track changes of the source documents. Most of the systems on the market can also perform ‘context match’, which is the identical match with identical surrounding segments in the translation document and in the translation memory. 3 The availability of networked or online systems Because of the fast development of new information technologies, most CAT systems during this period were server-based, web-based and even cloud-based CAT systems, which had a huge storage of data. By the end of 2012, there were fifteen cloud-based CAT systems available on the market for individuals or enterprises, such as Lingotek Collaborative Translation Platform, SDL World Server, and XTM Cloud. 4 The adoption of new formats in the industry Data exchange between different CAT systems has always been a difficult issue to handle as different systems have different formats, such as dvmdb for Déjà Vu X, and tmw for SDL Trados Translator’s Workbench 8.0. These program-specific formats cannot be mutually recognizable, which makes it impossible to share data in the industry. In the past, the Localization Industry Standards Association (LISA) played a significant role in developing and promoting data exchange standards, such as SRX (Segmentation Rules eXchange), TMX (Translation Memory eXchange), TBX (Term-Bese eXchange) and XLIFF (XML Localisation Interchange File Format). (http://en.wikipedia.org/wiki/XLIFF). It can be estimated that the compliance of industry standards is also one of the future directions for better data exchange. Translation technology on a fast track: a comparison of the developments of computer-aided translation with human translation and machine translation The speed of the development of translation technology in recent decades can be illustrated through a comparison of computer-aided translation with human translation and machine translation. The development of human translation Human translation, in comparison with machine translation and computer-aided translation, has taken a considerably longer time and slower pace to develop. The history of human translation can be traced to 1122 bc when during the Zhou dynasty (1122–255 bc), a foreign S. Chan Development of translation technology23 affairs bureau known as Da xing ren 大行人 was established to provide interpreting services for government officials to communicate with the twelve non-Han minorities along the borders of the Zhou empire (Chan 2009: 29−30). This is probably the first piece of documentary evidence of official interpreting in the world. Since then a number of major events have taken place in the world of translation. In 285 bc, there was the first partial translation of the Bible from Hebrew into Greek in the form of the Septuagint (Worth 1992: 5−19). In 250 bc, the contribution of Andronicus Livius to translation made him the ‘father of translation’ (Kelly 1998: 495−504). In 67, Zhu Falan made the first translation of a Buddhist sutra in China (Editorial Committee 1988: 103). In 1141, Robert de Retines produced the first translation of the Koran in Latin (Chan 2009: 47). In 1382, John Wycliffe made the first complete translation of the Bible in English (Worth 1992: 66−70). In 1494, William Tyndale was the first scholar to translate the Bible from the original Hebrew and Greek into English (Delisle and Woodsworth 1995: 33−35). In 1611, the King James Version of the Bible was published (Allen 1969). In 1814, Robert Morrison made the first translation of the Bible into Chinese (Chan 2009: 73). In 1945, simultaneous interpreting was invented at the Nuremberg Trials held in Germany (Gaiba 1998). In 1946, the United Bible Societies was founded in New York (Chan 2009: 117). In 1952, the first conference on machine translation was held at the Massachusetts Institute of Technology (Hutchins 2000: 6, 34−35). In 1953, the Fédération Internationale des Traducteurs (FIT), or International Association of Translators, and the Association Internationale des Interprètes de Conference (AIIC), or the International Association of Conference Interpreters, were both founded in Paris (Haeseryn 1989: 379−84; Phelan 2001). In 1964, with the publication of Toward a Science of Translating in which the concept of dynamic equivalent translation was proposed, Eugene A. Nida was referred to as the ‘Father of Translation Theory’ (Nida 1964). In 1972, James S. Holmes proposed the first framework for translation studies (Holmes 1972/1987: 9−24, 1988: 93−98). In 1978, Even-Zohar proposed the Polysystem Theory (Even-Zohar 1978: 21−27). A total of some seventeen major events took place during the history of human translation, which may be 3,135 years old. This shows that in terms of the mode of production, human translation has remained unchanged for a very long time. The development of machine translation In comparison with human translation, machine translation has advanced enormously since its inception in the 1940s. This can be clearly seen from an analysis of the countries with research and development in machine translation during the last seventy years. Available information shows that an increasing number of countries have been involved in the research and development of machine translation. This is very much in evidence since the beginning of machine translation in 1947. Actually, long before the Second World War was over and the computer was invented, Georges Artsrouni, a French-Armenian engineer, created a translation machine known as ‘Mechanical Brain’. Later in the year, Petr Petrovi č Smirnov- Troyanskij (1894−1950), a Russian scholar, was issued a patent in Moscow on 5 September for his construction of a machine which could select and print words while translating from one language into another or into several others at the same time (Chan 2004: 289). But it was not until the years after the Second World War that the climate was ripe for the development of machine translation. The invention of computers, the rise of information theory, and the advances in cryptology all indicated that machine translation could be a reality. In 1947, the idea of using machines in translating was proposed in March by Warren Weaver (1894−1978), who was at that time the vice president of the Rockefeller Foundation, and 24 Andrew D. Booth of Birkbeck College of the University of London. They wanted to make use of the newly invented computer to translate natural languages. Historically speaking, their idea was significant in several ways. It was the first application of the newly invented computers to non-numerical tasks, i.e. translation. It was the first application of the computer to natural languages, which was later to be known as computational linguistics. It was also one of the first areas of research in the field of artificial intelligence. The following year witnessed the rise of information theory and its application to translation studies. The role of this theory has been to help translators recognize the function of concepts such as information load, implicit and explicit information, and redundancy (Shannon and Weaver 1949; Wiener 1954). On 15 July 1948, Warren Weaver, director of the Rockefeller Foundation’s natural sciences division, wrote a memorandum for peer review outlining the prospects of machine translation, known in history as ‘Weaver’s Memorandum’, in which he made four proposals to produce translations better than word-for-word translations (Hutchins 2000: 18−20). The first machine translation system, the Georgetown-IBM system for Russian−English translation, was developed in the United States in June 1952. The system was developed by Leon Dostert and Paul Garvin of Georgetown University and Cuthbert Hurd and Peter Sheridan of IBM Corporation. This system could translate from Russian into English (Hutchins 1986: 70−78). Russia was the second country to develop machine translation. At the end of 1954, the Steklov Mathematical Institute of the Academy of Sciences began work on machine translation under the directorship of Aleksej Andreevi č Ljapunov (1911−1973), a mathematician and computer expert. The first system developed was known as FR-I, which was a direct translation system and was also considered one of the first generation of machine translation systems. The system ran on STRELA, one of the first generation of computers (Hutchins 2000: 197−204). In the same year, the United Kingdom became the third country to engage in machine translation. A research group on machine translation, Cambridge Language Research Group, led by Margaret Masterman, was set up at Cambridge University, where an experimental system was tried on English-French translation (Wilks 2000: 279−298). In 1955, Japan was the fourth country to develop machine translation. Kyushu University was the first university in Japan to begin research on machine translation (Nagao 1993: 203−208). This was followed by China, which began research on machine translation with a Russian− Chinese translation algorithm jointly developed by the Institute of Linguistics and the Institute of Computing Technology (Dong 1988: 85−91; Feng 1999: 335−340; Liu 1984: 1−14). Two years later, Charles University in Czechoslovakia began to work on English–Czech machine translation (http://www.cuni.cz). These six countries were the forerunners in machine translation. Other countries followed suit. In 1959, France set up the Centre d’Études de la Traduction Automatique (CETA) for machine translation (Chan 2009: 300). In 1960, East Germany had its Working Group for Mathematical and Applied Linguistics and Automatic Translation, while in Mexico, research on machine translation was conducted at the National Autonomous University of Mexico (Universidad Nacional Autonoma de Mexico) (http://www.unam.mx). In 1962, Hungary’s Hungarian Academy of Sciences conducted research on machine translation. In 1964 in Bulgaria, the Mathematical Institute of the Bulgarian Academy of Sciences in Sofia set up the section of ‘Automatic Translation and Mathematical Linguistics’ to conduct work on machine translation (http://www.bas.bg; Hutchins 1986: 205−06). In 1965, the Canadian Research Council set up CETADOL (Centre de Traitement Automatique des Données Linguistiques) to work on an English−French translation system (Hutchins 1986: 224). S. Chan Development of translation technology25 But with the publication of the ALPAC Report prepared by the Automatic Language Processing Advisory Committee of the National Academy of Sciences, which concluded with the comment that there was ‘no immediate or predictable prospect of useful machine translation’, funding for machine translation in the United States was drastically cut and interest in machine translation waned considerably (ALPAC 1966; Warwick 1987: 22−37). Still, sporadic efforts were made in machine translation. An important system was developed in the United States by Peter Toma, previously of Georgetown University, known as Systran, an acronym for System Translation. To this day, this system is still one of the most established and popular systems on the market. In Hong Kong, The Chinese University of Hong Kong set up the Hung On-To Research Laboratory for Machine Translation to conduct research into machine translation and developed a practical machine translation system known as ‘The Chinese University Language Translator’, abbreviated as CULT (Loh 1975: 143−155, 1976a: 46−50, 1976b: 104−05; Loh, Kong and Hung 1978: 111−120; Loh and Kong 1979: 135−148). In Canada, the TAUM group at Montreal developed a system for translating public weather forecasts known as TAUM-METEO, which became operative in 1977. In the 1980s, the most important translation system developed was the EUROTRA system, which could translate all the official languages of the European Economic Community (Arnold and Tombe 1987: 1143−1145; Johnson, King and Tombe 1985: 155−169; King 1982; King 1987: 373−391; Lau 1988: 186−191; Maegaard 1988: 61−65; Maegaard and Perschke 1991: 73−82; Somers 1986: 129−177; Way, Crookston and Shelton 1997: 323−374). In 1983, Allen Tucker, Sergei Nirenburg, and others developed at Colgate University an AI-based multilingual machine translation system known as TRANSLATOR to translate four languages, namely English, Japanese, Russian, and Spanish. This was the beginning of knowledge-based machine translation in the United States (http://www.colgate.edu). The following year, Fujitsu produced ATLAS/I and ATLAS/II translation systems for translation between Japanese and English in Japan, while Hitachi and Market Intelligence Centre (QUICK) developed the ATHENE English−Japanese machine translation system (Chan 2009: 223). In 1985, the ArchTran machine translation system for translation between Chinese and English was launched in Taiwan and was one of the first commercialized English−Chinese machine translation systems in the world (Chen, Chang, Wang and Su 1993: 87−98). In the United States, the METAL (Mechanical Translation and Analysis of Languages) system for translation between English and German, supported by the Siemens Company in Munich since 1978 and developed at the University of Texas, Austin, became operative (Deprez, Adriaens, Depoortere and de Braekeleer 1994: 206−212; Lehmann, Bennett and Slocum et al. 1981; Lehrberger 1981; Little 1990: 94−107; Liu and Liro 1987: 205−218; Schneider 1992: 583−594; Slocum, Bennett, Bear, Morgan and Root 1987: 319−350; White 1987: 225−240). In China, the TranStar English−Chinese Machine Translation System, the first machine-translation product in China, developed by China National Computer Software and Technology Service Corporation, was commercially available in 1988 (http://www.transtar.com.cn). In Taiwan, the BehaviorTran, an English−Chinese machine translation system, was also launched in the same year. In the 1990s, Saarbrucken in Germany formed the largest and the most established machine translation group in 1996. The SUSY (Saarbrücker Ubersetzungssystem/The Saarbrücken Machine Translation System) project for German to English and Russian to German machine translation was developed between 1972 and 1986 (rz.uni-sb.de). In 1997, Dong Fang Kuai Che 東方快車 (Orient Express), a machine translation system developed by the China Electronic Information Technology Ltd. in China, was commercially available (Chan 2004: 336) while in Taiwan, TransBridge was developed for internet translation from English into Chinese (http:// 26 www.transbridge.com.tw). The first year of the twenty-first century witnessed the development of BULTRA (BULgarian TRAnslator), the first English−Bulgarian machine translation tool, by Pro Langs in Bulgaria (Chan 2004: 339).What has been presented above shows very clearly that from the beginning of machine translation in 1947 until 1957, six countries were involved in the research and development of machine translation, which included Massachusetts Institute of Technology and Georgetown University in the United States in 1952, Academy of Sciences in Russia and Cambridge University in the United Kingdom in 1954, Kyushu University in Japan in 1955, the Institute of Linguistics in China in 1956, and Charles University in Czechoslovakia in 1957. By 2007, it was found that of the 193 countries in the world, 30 have conducted research on computer or computer-aided translation, 9 actively. This means that around 16 per cent of all the countries in the world have been engaged in machine translation, 30 per cent of which are active in research and development. The 31 countries which have been engaged in the research and development of machine translation are: Belgium, Brazil, Bulgaria, Canada, China, Czechoslovakia, Denmark, Finland, France, Germany, Hungary, India, Italy, Japan, Korea, Macau, Malaysia, Mexico, the Netherlands, Luxemburg, Poland, Russia, Singapore, Slovenia, Spain, Sweden, Switzerland, Taiwan, Tunisia, the United Kingdom, and the United States. Of these, the most active countries are China and Japan in Asia, France, Germany, the Netherlands, the United Kingdom, and Russia in Europe, and Canada and the United States in North America. The huge increase in the number of countries engaged in machine translation and the fast development of systems for different languages and language pairs show that machine translation has advanced by leaps and bounds in the last 65 years. Conclusion It should be noted that computer-aided translation has been growing rapidly in all parts of the world in the last 47 years since its inception in 1967. Drastic changes have taken place in the field of translation since the emergence of commercial computer-aided translation systems in the 1980s. In 1988, as mentioned above, we only had the Trados system that was produced in Europe. Now we have more than 100 systems developed in different countries, including Asian countries such as China, Japan, and India, and the northern American countries, Canada and the United States. In the 1980s, very few people had any ideas about computer-aided translation, let alone translation technology. Now, it is estimated that there are around 200,000 computer-aided translators in Europe, and more than 6,000 large corporations in the world handle their language problems with the use of corporate or global management computer- aided translation systems. At the beginning, computer-aided translation systems only had standalone editions. Now, there are over seventeen different types of systems on the market. According to my research, the number of commercially available computer-aided translation systems from 1984 to 2012 is 86. Several observations on these systems can be made. First, about three computer-aided translation systems have been produced every year during the last 28 years. Second, because of the rapid changes in the market, nineteen computer-aided translation systems failed to survive in the keen competition, and the total number of current commercial systems stands at 67. Third, almost half of the computer-aided translation systems have been developed in Europe, accounting for 49.38 per cent, while 27.16 per cent of them have been produced in America. All these figures show that translation technology has been on the fast track in the last five decades. It will certainly maintain its momentum for many years to come. S. Chan Development of translation technology27 References Allen, Ward (ed.) (1969) Translating for King James, Nashville, TN: Vanderbilt University Press. ALPAC (Automatic Language Processing Advisory Committee) (1966) Languages and Machines: Computers in Translation and Linguistics, A Report by the Automatic Language Processing Advisory Committee, Division of Behavioral Sciences, National Academy of Sciences, National Research Council, Washington, DC: National Academy of Sciences, National Research Council, 1966. Arnold, Doug J. and Louis des Tombe (1987) ‘Basic Theory and Methodology in EUROTRA’, in Sergei Nirenburg (ed.) Machine Translation: Theoretical and Methodological Issues, Cambridge: Cambridge University Press, 114−135. Arthern, Peter J. (1979) ‘Machine Translation and Computerized Terminology Systems: A Translator’s viewpoint’, in Barbara M. Snell (ed.) Translating and the Computer: Proceedings of a Seminar, London: North-Holland Publishing Company, 77−108. Brace, Colin (1992) ‘Trados: Smarter Translation Software’, Language Industry Monitor Issue September– October. Available at: http://www.lim.nl/monitor/trados-1.html. Brace, Colin (1993) ‘TM/2: Tips of the Iceberg’, Language Industry Monitor Issue May−June. Available at: Retrieved from http://www.mt-archive. Brace, Colin (1994) ‘Bonjour, Eurolang Optimizer’, Language Industry Monitor Issue March-April. Available at: http://www.lim.nl/monitor/optimizer.html. Chan, Sin-wai (2004) A Dictionary of Translation Technology, Hong Kong: The Chinese University Press. Chan, Sin-wai (2009) A Chronology of Translation in China and the West, Hong Kong: The Chinese University Press. Chen, Gang (2001) ‘A Review on Yaxin CAT2.5’, Chinese Science and Technology Translators Journal 14(2). Chen, Shuchuan, Chang Jing-shin, Wang Jong-nae, and Su Keh-yih (1993) ‘ArchTran: A Corpus-based Statistics-oriented English-Chinese Machine Translation System’, in Sergei Nirenburg (ed.) Progress in Machine Translation, Amsterdam: IOP Press, 87−98. Deprez, F., Greert Adriaens, Bart Depoortere, and Gert de Braekeleer (1994) ‘Experiences with METAL at the Belgian Ministry of the Interior’, Meta 39(1): 206−212. Delisle, Jean and Judith Woodsworth (eds) (1995) Translators through History, Amsterdam and Philadelphia: John Benjamins Publishing Company and UNESCO Publishing. Dong, Zhendong (1988) ‘MT Research in China’, in Dan Maxwell, Klaus Schubert, and Toon Witkam (eds) New Directions in Machine Translation, Dordrecht, Holland: Foris Publications, 85−91. Editorial Committee, A Dictionary of Translators in China 《中國翻譯家詞典》編寫組 (ed.) (1988) 《中 國翻譯家詞典》 (A Dictionary of Translators in China), Beijing: China Translation and Publishing Corporation 中國對外翻譯出版公司. Elita, Natalia and Monica Gavrila (2006) ‘Enhancing Translation Memories with Semantic Knowledge’, Proceedings of the 1 st Central European Student Conference in Linguistics, 29-31 May 2006, Budapest, Hungary: 24−26. Eurolux Computers (1992) ‘Trados: Smarter Translation Software’, Language Industry Monitor 11, September−October. Available at: http://www.lim.nl. Even-Zohar, Itamar (1978) Papers in Historical Poetics, Tel Aviv: The Porter Institute for Poetics and Semiotics, Tel Aviv University. Feng, Zhiwei 馮志偉 (1999)〈中國的翻譯技術:過去、現在和將來〉(Translation Technology in China: Past, Present, and Future), in Huang Changning 黃昌寧 and Dong Zhendong 董振東 (eds) 《計算器語言學文集》(Essays on Computational Linguistics), Beijing: Tsinghua University Press, 335– 440. Gaiba, Francesca (1998) The Origins of Simultaneous Interpretation: The Nuremberg Trial, Ottawa: University of Ottawa Press. Garcia, Ignacio and Vivian Stevenson (2005) ‘Trados and the Evolution of Language Tools: The Rise of the De Facto TM Standard – And Its Future with SDL’, Multilingual Computing and Technology 16(7). Garcia, Ignacio and Vivian Stevenson (2006) ‘Heartsome Translation Suite’, Multingual 17(1): 77. Available at: http://www.multilingual.com. German, Kathryn (2009) ‘Across: An Exciting New Computer Assisted Translation Tool’, The Northwest Linguist 9−10. Gotti, Fabrizio, Philippe Langlais, Elliott Macklovitch, Didier Bourigault, Benoit Robichaud, and Claude Coulombe (2005) ‘3GTM: A Third-generation Translation Memory’, Proceedings of the 3 rd Computational Linguistics in the North-East (CLiNE) Workshop, Gatineau, Québec, Canada, 26–30. 28 Haeseryn, Rene (1989) ‘The International Federation of Translators (FIT) and its Leading Role in the Translation Movement in the World’, in Rene Haeseryn (ed.) Roundtable Conference FIT-UNESCO: Problems of Translator in Africa, Belgium: FIT, 379−384. Hall, Amy (2000) ‘SDL Announces Release of SDLX Version 2.0’, SDL International. Available at: http://www.sdl.com/en/about-us/press/1999/SDL_Announces_Release_of_SDLX_Version_2_0. asp. Harmsen, R. (2008) ‘Evaluation of DVX’. Available at: http://rudhar.com. Holmes, James S. (1972, 1987) ‘The Name and Nature of Translation Studies’, in Gideon Toury (ed.) Translation across Cultures, New Delhi: Bahri Publications: Pvt. Ltd., 9−24. Holmes, James S. (1988) ‘The Name and Nature of Translation Studies’, in James S. Holmes (ed.) Translated! Papers on Literary Translation and Translation Studies, Amsterdam: University of Amsterdam, 93−98. http://anaphraseus.sourceforge.net. http://blog.memsource.com. http://developer.apple.com. http://en.wikipedia.org/wiki/XLIFF http://felix-cat.com. http://new.multilingual.com. http://terminotix.com/news/newsletter. http://www.across.net. http://www.alchemysoftware.ie. http://www.atasoft.com. http://www.atril.com. http://www.bas.bg. http://www.colgate.edu. http://www.cuni.cz. http://www.dreye.com.tw. http://www.gcys.cn. http://www.heartsome.net. http://www.hjtek.com. http://www.hjtrans.com. http://www.kilgray.com. http://www.lisa.org. http://www.logovista.co.jp. http://www.maxprograms.com. http://www.memsource.com. http://www.metatexis.com. http://www.multicorpora.com. http://www.multilizer.com. http://www.omegat.org. http://www.opentm2.org. http://www.otek.com.tw. http://www.promt.com. http://www.sdl.com. http://www.sdlintl.com. http://www.star-group.net. http://www.systransoft.com. http://www.thelanguagedirectory.com/translation/translation_software. http://www.transbridge.com.tw. http://www.translationzone.com. http://www.transn.com. http://www.transtar.com.cn. http://www.tratool.com. http://www.unam.mx. http://www.urelitetech.com.cn. http://www.wordbee.com. http://www.wordfast.com. http://wordfast.net/champollion.net. S. Chan Development of translation technology29 http://www.xtm-intl.com. http://www.zklr.com. Hutchins, W. John (1986) Machine Translation: Past, Present and Future, Chichester: Ellis Horwood. Hutchins, W. John (1998) ‘The Origins of the Translator’s Workstation’, Machine Translation 13(4): 287−307. Hutchins, W. John (1999) ‘The Development and Use of Machine Translation System and Computer- based Translation Tools’, in Chen Zhaoxiong (ed.) Proceedings of the International Conference on MT and Computer Language Information Processing, Beijing: Research Center of Computer and Language Engineering, Chinese Academy of Sciences, 1−16. Hutchins, W. John (2000) Early Years in Machine Translation, Amsterdam and Philadelphia: John Benjamins. Johnson, R.I., Margaret King, and Louis des Tombe (1985) ‘Eurotra: A Multilingual System under Development’, Computational Linguistics 11(2−3): 155−169. Kavak, Pinar (2009) ‘Development of a Translation Memory System for Turkish to English’, Unpublished MA dissertation, Bo ğaziçi University, Turkey. Kay, Martin (1980) ‘The Proper Place of Men and Machines in Language Translation’, Research Report CSL-80-11, Xerox Palo Alto Research Center, Palo Alto, CA. Kelly, Louis G. (1998) ‘Latin Tradition’, in Mona Baker (ed.) Routledge Encyclopedia of Translation Studies, London and New York: Routledge, 495−504. Kilgrary Translation Technologies (2011) ‘What’s New in MemoQ’. Available at: http://kilgray.com/ products/memoq/whatsnew. King, Margaret (1982) EUROTRA: An Attempt to Achieve Multilingual MT, Amsterdam: North-Holland. King, Margaret (ed.) (1987) Machine Translation Today: The State of the Art, Edinburgh: Edinburgh University Press. Környei, Tibor (2000) ‘WordFisher for MS Word: An Alternative to Translation Memory Programs for Freelance Translators?’ Translation Journal 4(1). Available at: http://accurapid.com/journal/11wf.htm. Lau, Peter Behrendt (1988) ‘Eurotra: Past, Present and Future’, in Catriona Picken (ed.) Translating and the Computer 9: Potential and Practice, London: The Association for Information Management, 186−91. Lehmann, Winfred P., Winfield S. Bennett, Jonathan Slocum et al. (1981) The METAL System, New York: Griffiss Air Force Base. Lehrberger, John (1981) The Linguistic Model: General Aspects, Montreal: TAUM Group, University of Montreal. LISA (2010) ‘IBM and the Localization Industry Standards Association Partner to Deliver Open-Source Enterprise-level Translation Tools’. Available at: http://www.lisa.org/OpenTM2.1557.0.html. Little, Patrick (1990) ‘METAL – Machine Translation in Practice’, in Catriona Picken (ed.) Translation and the Computer 11: Preparing for the Next Decade, London: The Association for Information Management, 94−107. Liu, Jocelyn and Joseph Liro (1987) ‘The METAL English-to-German System: First Progress Report’, Computers and Translation 2(4): 205−218. Liu, Yongquan et al. 劉湧泉等 (1984) 《中國的機器翻譯》(Machine Translation in China), Shanghai: Knowledge Press. Locke, Nancy A and Marc-Olivier Giguère (2002) ‘MultiTrans 3.0’, MultiLingual Computing and Technology 13(7): 51. Locke, William Nash and Andrew Donald Booth (eds) (1955) Machine Translation of Languages: Fourteen Essays, Cambridge, MA: MIT Press. Loh, Shiu-chang (1975) ‘Machine-aided Translation from Chinese to English’, United College Journal 12(13): 143−155. Loh, Shiu-chang (1976a) ‘CULT: Chinese University Language Translator’, American Journal of Computational Linguistics Microfiche, 46, 46−50. Loh, Shiu-chang (1976b) ‘Translation of Three Chinese Scientific Texts into English by Computer’, ALLC Bulletin 4(2): 104−05. Loh, Shiu-chang, Kong Luan, and Hung Hing-sum (1978) ‘Machine Translation of Chinese Mathematical Articles’, ALLC Bulltein 6(2): 111−120. Loh, Shiu-chang and Kong Luan (1979) ‘An Interactive On-line Machine Translation System (Chinese into English)’, in Barbara M. Snell (ed.) Translating and the Computer, Amsterdam: North-Holland, 135−148. Maegaard, Bente (1988) ‘EUROTRA: The Machine Translation Project of the European Communities’, Literary and Linguistic Computing 3(2): 61−65. 30 Maegaard, Bente and Sergei Perschke (1991) ‘Eurotra: General Systems Design’, Machine Translation 6(2): 73−82. Melby, Alan K. (1978) ‘Design and Implementation of a Machine-assisted Translation System’, Proceedings of the 7 th International Conference on Computational Linguistics, 14−18 August 1978, Bergen, Norway. Melby, Alan K. and Terry C. Warner (1995) The Possibility of Language: A Discussion of the Nature of Language, with Implications for Human and Machine Translation, Amsterdam and Philadelphia: John Benjamins. MultiCorpora Inc. (2011) ‘MultiCorpora Launches New Translation Management System’. Available at: http://www.multicorpora.com/news/multicorpora-launches-new-translation-management-system. MultiLingual (1997) ‘CIMOS Releases Arabic to English Translation Software’, MultiLingual 20 December 1997. Available at: http://multilingual.com/newsDetail.php?id=422 MultiLingual (1998) ‘SDL Announces Translation Tools’, MultiLingual 23 September 1998. Available at: http://multilingual.com/newsDetail.php?id=154. MultiLingual (1999) ‘SDL Announces SDL Workbench and Product Marketing Executive’, MultiLingual 22 Feburary 1999. Available at: http://multilingual.com/newsDetail.php?id=12. MultiLingual (2003) ‘MultiCorpora R&D Releases MultiTrans 3.5’, MultiLingal 17 October 2003. Available at: http://multilingual.com/newsDetail.php?id=3219. MultiLingual (2005a). ‘STAR Releases Transit Service Pack 14’, MultiLingual 15 April 2005. Available at: http://multilingual.com/newsDetail.php?id=4169. MultiLingual (2005b) ‘SIMILIS Version 1.4 Released’, MultiLingual 27 April 2005. Available at: http:// multilingual.com/newsDetail.php?id=4187. MultiLingual (2005c) ‘SDL Releases SDLX 2005’, MultiLingual 5 May 2005. Available at: http:// multilingual.com/newsDetail.php?id=4216. MultiLingual (2005d) ‘MultiCorpora Announces the Release of MultiTrans 4’, MultiLingual 31 August 2005. Available at: http://multilingual.com/newsDetail.php?id=4425. MultiLingual (2006a) ‘Across Rolls out New Version 3.5’, MultiLingual 20 November 2006. Available at: http://multilingual.com/newsDetail.php?id=5372. MultiLingual (2006b) ‘Lingotek Announces Beta Launch of Language Search Engine’, MultiLingual 15 August 2006. Available at: http://multilingual.com/newsDetail.php?id=5168. MultiLingual (2007) ‘Kilgray Releases Version 2.0 of MemoQ’, MultiLingual 25 January 2007. Available at: http://multilingual.com/newsDetail.php?id=5467. MultiLingual (2008a) ‘Across Language Server 4.0 SP1’, MultiLingual 21 April 2008. Available at: http:// multilingual.com/newsDetail.php?id=6228. MultiLingual (2008b) ‘Fusion One and Fusion Collaborate 3.0’, MultiLingual 28 November 2008. Available at: http://multilingual.com/newsDetail.php?id=6568. MultiLingual (2009a) ‘Fusion 3.1’, MultiLingual 19 March 2009. Available at: http://multilingual.com/ newsDetail.php?id=6734. MultiLingual (2009b) ‘Across Language Server V.5’, MultiLingual 13 May 2009. Available at: http:// multilingual.com/newsDetail.php?id=6834. MultiLingual (2009c) ‘Lingotek Launches Crowdsourcing Translation Platform’, MultiLingual 22 October 2009. Available at: http://multilingual.com/newsDetail.php?id=7103. MultiLingual (2010a) ‘SDL Trados Studio’, MultiLingual 3 March 2010. Available at: http://multilingual. com/newsDetail.php?id=7298. MultiLingual (2010b) ‘Collaborative Translation Platform 5.0’, MultiLingual 27 July 2010. Available at: http://multilingual.com/newsDetail.php?id=7544. MultiLingual (2011) ‘Déjà Vu X2’, MultiLingual 24 May 2011. Available at: http://multilingual.com/ newsDetail.php?id=933. Nagao, Makoto (1993) ‘Machine Translation: The Japanese Experience’, in Sergei Nirenburg (ed.) Progress in Machine Translation, Amsterdam: IOS Press, 203−208. Nida, Eugene A. (1964) Toward a Science of Translating, Leiden: E.J. Brill. Phelan, Mary (2001) The Interpreter’s Resource, Clevedon: Multilingual Matters Ltd. Prior, Marc (2003) ‘Close Windows, Open Doors’, Translation Journal 7(1). Available at: http://accurapid. com/journal/23linux.htm. Schmidt, Axel (2006) ‘Integrating Localization into the Software Development Process’, TC World March 2006. Schneider, Thomas (1992) ‘User Driven Development: METAL as an Integrated Multilingual System’, Meta 37(4): 583−594. S. Chan Development of translation technology31 Shannon, Claude L. and Warren Weaver (1949) The Mathematical Theory of Communication, Urbana, IL: University of Illinois Press. Slocum, Jonathan, Winfield S. Bennett, J. Bear, M. Morgan, and Rebecca Root (1987) ‘METAL: The LRC Machine Translation System’, in Margaret King (ed.) Machine Translation Today: The State of the Art, Edinburgh: Edinburgh University Press, 319−350. Somers, Harold L. (1986) ‘Eurotra Special Issue’, Multilingual 5(3): 129−177. Sumita, Eiichiro and Yutaka Tsutsumi (1988) ‘A Translation Aid System Using Flexible Text Retrieval Based on Syntax-matching’, in Proceedings of the 2 nd International Conference on Theoretical and Methodological Issues in Machine Translation of Natural Languages, Pittsburgh, Pennsylvania: Carnegie Mellon University. Available online at: http:// www.mt-archive. info/TMI-1988-Sumita.pdf. Wang, Zheng 王正 (2011) 〈翻譯記憶系統的發展歷程與未來趨勢〉(Translation Memory Systems: A Historical Sketch and Future Trends),《編譯論叢》(Compilation and Translation Review) 4(1): 133−160. Warwick, Susan (1987) ‘An Overview of Post-ALPAC Developments’, in Margaret King (ed.) Machine Translation Today: The State of the Art, Edinburgh: Edinburgh University Press, 22−37. Wassmer, Thomas (2003) ‘SDLX TM Translation Suite 2003’, Translation Journal 7(3). Wassmer, Thomas (2004) ‘Trados 6.5’, MultiLingual Computing and Technology 15(1): 61. Wassmer, Thomas (2007) ‘Comparative Review of Four Localization Tools: Déjà Vu, MULTILIZER, MultiTrans and TRANS Suite 2000, and Their Various Capabilities’, MultiLingual Computing and Technology 14(3): 37−42. Wassmer, Thomas (2011) ‘Dr Tom’s Independent Software Reviews’. Available at: http://www. localizationworks.com/DRTOM/Trados/TRADOS. Way, Andrew, Ian Crookston, and Jane Shelton (1997) ‘A Typology of Translation Problems for Eurotra Translation Machines’, Machine Translation 12(4): 323−374. White, John S. (1987) ‘The Research Environment in the METAL Project’, in Sergei Nirenburg (ed.) Machine Translation: Theoretical and Methodological Issues, Cambridge: Cambridge University Press, 225−240. Wiener, Norbert (1954) The Human Use of Human Beings: Cybernetics and Society, New York: Houghton Mifflin. Wilks, Yorick (2000) ‘Magaret Masterman’, in W. John Hutchins (ed.) Early Years in Machine Translation, Amsterdam and Philadelphia: John Benjamins Publishing Company, 279−298. Worth, Roland H. (1992) Bible Translations: A History through Source Documents, Jefferson, NC, and London: McFarland and Company, Inc., Publishers. Xu, Jie (2001) ‘Five Amazing Functions of Dr Eye 2001’ (Dr Eye 2001 譯典通 5 大非凡功能), 《廣東 電腦與電訊》(Computer and Telecom) (3). Yngve, Victor H. (2000) ‘Early Research at M.I.T. in Search of Adequate Theory’, in W. John Hutchins (ed.) Early Years in Machine Translation, Amsterdam and Philadelphia: John Benjamins, 39−72. Zhang, Zheng 張政 (2006)《計算機翻譯研究》(Studies on Machine Translation), Beijing: Tsinghua University Press 清華大學出版社. 32 2 COMPUTER-AIDED TRANSLATION Major concepts Chan Sin-wai the chinese university ff hfng kfng, hfng kfng, china Introduction When the term computer-aided translation is mentioned, we often associate it with the functions a computer-aided translation system offers, such as toolbars, icons, and hotkeys, the built-in tools we can use, such as online dictionaries, browsers, and the computational hitches we often encounter when working on a computer-aided translation project, such as chaotic codes. What is more important is to see beyond the surface of computer-aided translation to find out the major concepts that shape the development of functions in translation technology. Concepts, which are relatively stable, govern or affect the way functions are designed and developed, while functions, which are fast-changing, realize the concepts through the tasks they perform. As a major goal of machine translation is to help human translators, a number of functions in computer-aided translation systems have been created to enable machine processing of the source with minimum human intervention. Concepts, moreover, are related to what translators want to achieve in translating. Simply put, translators want to have a controllable (controllability) and customizable (customizability) system, which is compatible with file formats (compatibility) and language requirements, and behaves as well as (simulativity) or even better than (emulativity) a human translator, to allow them to work together (collaborativity) to produce quality translations (productivity). We have therefore identified seven major concepts which are important in computer-aided translation: simulativity, emulativity, productivity, compatibility, controllability, customizability, and collaborativity. The order in which concepts are arranged can be memorized more easily by their acronym SEPCCCC. Simulativity The first concept of computer-aided translation is simulativity, which is about the way in which a computer-aided translation system models the behaviour of a human translator by means of its functions, such as the use of concordancers in text analysis to model after comprehension on the part of the human translator and the creation of a number of quality assurance tools to follow the way checking is done by a human translator. There are a number of ways to illustrate man–machine simulativity. Computer-aided translation33 (1) Goal of translation The first is about the ultimate goal of translation technology. All forms of translation (machine translation, computer-aided translation and human translation) aim at obtaining high-quality translations. In the case of machine translation, the goal of a fully automatic high-quality translation (FAHQT) is to be achieved through the use of a machine translation system without human intervention. In the case of computer-aided translation, the same goal is to be achieved through a computer-aided translation system that simulates the behaviour of a human translator through man−machine interaction. (2) Translation procedure A comparison of the procedures of human translation with those of computer-aided translation shows that the latter simulates the former in a number of ways. In manual translation, various translation procedures have been proposed by translation scholars and practitioners, ranging from the two-stage models to eight-stage ones, depending on the text type and purposes of translation. In machine translation and computer-aided translation, the process is known as technology-oriented translation procedure. (a) Two-stage model In human translation, the first type of translation procedure is a two-stage one, which includes the stage of source text comprehension and the stage of target text formulation, as shown below: Figure 2.1 A two-stage model for human translation Figure 2.1 is a model for human translators with the ability of comprehension. As a computer- aided translation system does not have the ability of comprehension, it cannot model after human translation with this two-stage model. It can, however, work on a two-stage translation with the use of its system dictionary, particularly in the case of a language-pair-specific system, as in Figure 2.2: Figure 2.2 A two-stage dictionary-based language-pair-specific model 34 Another two-stage model of computer-aided translation is a terminology-based system, as shown in Figure 2.3: Figure 2.3 A two-stage terminology-based CAT system (b) Three-stage models The second type of translation procedure is a three-stage model. This section covers five variations of this model proposed by Eugene Nida and Charles Taber (1969), Wolfram Wilss (1982), Roger Bell (1991), Basil Hatim and Ian Mason, and Jean Delisle (1988) respectively. A three-stage example-based computer-aided translation system is shown to illustrate the simulation of human translation by computer-aided translation. (i) model by eugene nida and charles taber The first model of a three-stage translation procedure involving the three phases of analysis, transfer, and restructuring was proposed by Eugene Nida and Charles Taber ([1969] 1982: 104). They intended to apply elements of Chomsky’s transformational grammar to provide Bible translators with some guidelines when they translate ancient source texts into modern target texts, which are drastically different in languages and structures. Nida and Taber describe this three-stage model as a translation procedure in which the translator first analyses the message of the source language into its simplest and structurally clearest forms, transfers it at this level, and then restructures it to the level in the receptor language which is most appropriate for the audience which he intends to reach. (Nida and Taber [1969] 1982: 484) Analysis is described by these two scholars as ‘the set of procedures, including back transformation and componential analysis, which aim at discovering the kernels underlying the source text and the clearest understanding of the meaning, in preparation for the transfer’ (Nida and Taber [1969] 1982: 197). Transfer, on the other hand, is described as the second stage ‘in which the analysed material is transferred in the mind of the translator from language A to language B’ (ibid.: 104). Restructuring is the final stage in which the results of the transfer process are transformed into a ‘stylistic form appropriate to the receptor language and to the intended receptors’. S. Chan Computer-aided translation35 In short, analysis, the first stage, is to analyse the source text, transfer, the second stage, is to transfer the meaning, and restructuring, the final stage, is to produce the target text. Their model is shown in Figure 2.4. Figure 2.4 Three-stage model by Nida and Taber (1964) (ii) model by wolfram wilss The second three-stage model was proposed by Wolfram Wilss (1982) who regards translation procedure as a linguistic process of decoding, transfer and encoding. His model is shown in Figure 2.5. Figure 2.5 Three-stage model by Wolfram Wilss (1982) (iii) model by roger bell Another three-stage model of note is by Roger Bell whose translation procedure framework is divided into three phases: the first phase is source text interpretation and analysis, the second, translation process, and the third, text reformulation (see Figure 2.6). The last phase takes into consideration three factors: writer’s intention, reader’s expectation, and the target language norms (Bell 1991). 36 Figure 2.6 Model of Roger Bell (iv) model by basil hatim and ian mason This model, proposed by Basil Hatim and Ian Mason, is a more sophisticated three-stage model, which involves the three steps of source text comprehension, transfer of meaning, and target text assessment (see Figure 2.7). At the source text comprehension level, text parsing, specialized knowledge, and intended meaning are examined. At the meaning transfer stage, consideration is given to the lexical meaning, grammatical meaning, and rhetorical meaning. At the target text assessment level, attention is paid to text readability, target language conventions, and the adequacy of purpose. Figure 2.7 A three-stage model by Basil Hatim and Ian Mason (v) model of jean delisle The fourth model of a three-stage translation procedure was proposed by Jean Delisle (1988: 53−69) (see Figure 2.8). Deslisle believes that there are three stages in the development of a translation equivalence: comprehension, reformulation, and verification: ‘comprehension is S. Chan Computer-aided translation37 based on decoding linguistic signs and grasping meaning, reformulation is a matter of reasoning by analogy and re-wording concepts, and verification involves back-interpreting and choosing a solution’ (1988: 53).Parallel to human translation, a three-stage model in computer-aided translation is the example-based system. The input text goes through the translation memory database and glossary database to generate fuzzy matches and translations of terms before getting the target text. The procedure of an example-based computer-aided translation system is shown in Figure 2.9. Figure 2.8 A three-stage model of Jean Delisle Figure 2.9 Three-stage example-based computer-aided translation model (c) Four-stage model The third type of translation procedure is a four-stage one. A typical example is given by George Steiner ([1975] 1992) who believes that the four stages of translation procedure are: knowledge of the author’s times, familiarization with author’s sphere of sensibility, original text decoding, and target text encoding (see Figure 2.10). 38 Figure 2.10 Model of George Steiner (1975) For computer-aided translation, a four-stage model is exemplified by webpage translation provided by Yaxin. The first stage is to input the Chinese webpage, the second stage is to process the webpage with the multilingual maintenance platform, the third stage is to process it with the terminology database, and the final stage is to generate a bilingual webpage. The Yaxin translation procedure is shown in Figure 2.11. Figure 2.11 Yaxin’s four-stage procedure (d) Five-stage model The fourth type of translation procedure is a five-stage one, as proposed by Omar Sheikh Al- Shabab (1996: 52) (see Figure 2.12). The first stage is to edit the source text, the second, interpret the source text, the third, interpret it in a new language, the fourth, formulate the translated text, and the fifth, edit the formulation.In computer-aided translation, a five-stage model is normally practised. At the first stage, the Initiating Stage, tasks such as setting computer specifications, logging in a system, creating a profile, and creating a project file are performed. At the second stage, the Data Preparation Stage, the tasks involve data collection, data creation, and the creation of terminology and translation memory databases. At the third stage, the Data Processing Stage, the tasks include data analysis, the use of system and non-system dictionaries, the use of concordancers, doing S. Chan Computer-aided translation39 Figure 2.12 Model of Omar Sheikh Al-Shabab pre-translation, data processing by translation by computer-aided translation systems with human intervention, or by machine translation systems without human intervention, or data processing by localization systems. At the fourth stage, the Data Editing Stage, the work is divided into two types. One type is data editing for computer-aided translation systems, which is about interactive editing, the editing environments, matching, and methods used in computer-aided translation. Another type is data editing for computer translation systems, which is about post-editing and the methods used in human translation. At the last or fifth stage, the Finalizing Stage, the work is mainly updating databases. The fives stages in computer-aided translation are illustrated in Figure 2.13. Figure 2.13 Five-stage technology-oriented translation procedure model It can be seen that though there are both five-stage models in human translation and computer- aided translation and the tasks involved are different, the concept of simulativity is at work at almost all stages. (e) Eight-stage model The fifth type of translation procedure is an eight-stage one, as proposed by Robert Bly (1983). 40 Robert Bly, who is a poet, suggests an eight-stage procedure for the translation of poetry: (a) set down a literal version; (b) find out the meaning of the poem; (c) make it sound like English; (d) make it sound like American; (e) catch the mood of the poem; (f) pay attention to sound; (g) ask a native speaker to go over the version; and (h) make a final draft with some adjustments (see Figure 2.14). Figure 2.14 Model by Robert Bly (1983) In computer-aided translation, there is no eight-stage model. But other than the five-stage model, there is also a seven-stage model, which is shown in Figure 2.15. Figure 2.15 Seven-stage computer-aided translation procedure The seven stages of computer-aided translation go from sample text collection to termbase creation, translation memory database creation, source text selection, data retrieval, source text translation and finally data updating. S. Chan Computer-aided translation41 All in all, we can say that when compared to human translation, computer-aided translation is simulative, following some of the stages in human translation. Emulativity There are obviously some functions which are performable by a computer-aided translation system, but not by a human translation. This is how technology can emulate human translation. Computer-aided translation, with the help of machine translation, simulates human translation, and it also emulates human translation in a number of areas of computer- aided translation, some of which are mentioned below. Alt-tag translation This function of machine translation allows the user to understand the meaning of text embedded within images (Joy 2002). The images on a web site are created by IMG tag (inline image graphic tag), and the text that provides an alternative message to viewers who cannot see the graphics is known as ALT-tag, which stands for ‘alternative text’. Adding an appropriate ALT-tag to every image within one’s web site will make a huge difference to its accessibility. As translators, our concern is the translation of the alternative text, as images are not to be translated anyway. Chatroom translation Machine translation has the function to translate the contents of a chatroom, known as ‘chat translation’ or ‘chatroom translation’. Chat translation systems are commercially available for the translation of the contents of the Chatroom on the computer. As a chat is part of conversational discourse, all the theoretical and practical issues relating to conversational discourse can be applied to the study of chat translation. It should be noted that this kind of online jargon and addressivity are drastically different from what we have in other modes of communication. The function of Chatroom is available in some systems, such as Fluency, as one of the resources. This function has to be purchased and enabled in the Fluency Chat Server to allow clients to be connected to this closed system for internal communications. For standalone version users, the function of Chat will be provided by Fluency Chat Server provided by its company, the Western Standard (Western Standard 2011: 39). Clipboard translation This is to copy a text to the clipboard from any Windows application for a machine translation system to translate the clipboard text, and the translated text can then be pasted in the original or any other location. One of the systems that translate clipboards is Atlas. Conversion between metric and British systems A function that can be easily handled by machine translation but not so easily by human translation is the conversion of weight, volume, length, or temperature from metric to British or vice versa. Fluency, for example, can do the metric/British conversion the target text box with the converted units. 42 Currency conversion Some computer-aided translation systems can do currency conversion. With the use of Currency Converter, a function in Fluency, and access to the Internet to get the currency conversion rates, systems can convert a currency in a country into the local country currency. The number of currencies that can be handled by a system is relatively large. Fluency, for example, supports the conversion of currencies of around 220 countries. The conversion of multiple currencies is also supported. Email translation This refers to the translation of emails by a machine translation system (Matsuda and Kumai 1999; Rooke 1985: 105−115). The first online and real-time email translation was made in 1994 by the CompuServe service which provided translation service of emails from and to English and French, German or Spanish. Email translation has since become a very important part of daily communication and most web translation tools have email translators to translate emails. As emails are usually conversational and often written in an informal or even ungrammatical way, they are difficult for mechanical processing (Fais and Ogura 2001; Han, Gates and Levin 2006). One of the systems that translates emails is Atlas. Foreign language translation One of the most important purposes of using translation software is to translate a source text the language of which is unfamiliar to the user so as to explain its contents in a language familiar to the user. It is found that a majority of the commercial machine translation systems are for translation among Indo-European languages or major languages with a large number of speakers or users. Software for translation between major languages and minor languages are relatively small in number. Gist translation Another area where machine translation differs fundamentally from human translation is gist translation, which refers to a translation output which expresses only a condensed version of the source text message. This type of rough translation is to get some essential information about what is in the text for a user to decide whether to translate it in full or not to serve some specific purposes. Highlight and translate This function allows the user to highlight a part of the text and translate it into the designated language. The highlighted text is translated on its own without affecting the rest of the text. Instant transliteration This refers to a function of machine translation which can transliterate the words of a text with a certain romanization system. In the case of Chinese, the Hanyu Pinyin Romanization system for simplified characters is used in mainland China, while the Wade-Giles Romanization system for traditional characters is used in Taiwan. S. Chan Computer-aided translation43 Mouse translation This is to translate sentences on a web page or on applications by simply clicking the mouse. Systems that provide mouse translation include Atlas. Online translation This is the translation of a text by an online machine translation system which is available at all times on demand from users. With the use of online translation service, the functions of information assimilation, message dissemination, language communication, translation entertainment, and language learning can be achieved. Pre-translation Machine translation is taken to be pre-translation in two respects. The first is as a kind of preparatory work on the texts to be translated, including the checking of spelling, the compilation of dictionaries, and the adjustment of text format. The second is taken to be a draft translation of the source text which can be further revised by a human translator. Sentence translation Unlike human translation which works at the textual level, machine translation is sentential translation. In other words, machine translation is a sentence-by-sentence translation. This type of translation facilitates the work of post-editing and methods which are frequently used in translating sentences in translation practice to produce effective translations can be used to produce good translations from machine translation systems. Web translation This refers to the translation of information on a web page from one language into another. Web-translation tools are a type of translation tools which translate information on a web page from one language into another. They serve three functions: (1) as an assimilation tool to transmit information to the user; (2) as a dissemination tool to make messages comprehensible; and (3) as a communication tool to enable communication between people with different language backgrounds. Productivity As translation technology is a field of entrepreneurial humanities, productivity is of great importance. Productivity in computer-aided translation is achieved through the use of technology, collective translation, recycling translations, reusing translations, professional competence, profit-seeking, labour-saving, and cost-saving. Using technology to increase productivity The use of technology to increase productivity needs no explanation. As early as 1980, when Martin Kay discussed the proper place of men and machines in language translation, he said: 44 Translation is a fine and exacting art, but there is much about it that is mechanical and routine and, if this were given over to a machine, the productivity of the translator would not only be magnified but his work would become more rewarding, more exciting, more human.(Kay 1980: 1) All computer-aided translation systems aim to increase translation productivity. In terms of the means of production, all translation nowadays is computer-aided translation as virtually no one could translate without using a computer. Collective translation to increase productivity Gone are the days when bilingual competence, pen and paper, and printed dictionaries made a translator. Gone are the days when a single translator did a long translation project all by himself. It is true that in the past, translation was mainly done singly and individually. Translation was also done in a leisurely manner. At present, translation is done largely through team work linked by a server-based computer-aided translation system. In other words, translation is done in a collective manner. Recycling translations to increase productivity To recycle a translation in computer-aided translation is to use exact matches automatically extracted from a translation memory database. To increase productivity, the practice of recycling translations is followed in computer-aided translation. Networked computer-aided translation systems are used to store centralized translation data, which are created by and distributed among translators. As this is the case, translators do not have to produce their own translations. They can simply draw from and make use of the translations stored in the bilingual database to form their translation of the source text. Translation is therefore produced by selection. Reusing translations to increase productivity To reuse a translation in computer-aided translation is to appropriate terms and expressions stored in a term database and translation memory database. It should be noted that while in literary translation, translators produce translations in a creative manner, translators in practical translation reuse and recycle translations as the original texts are often repetitive. In the present age, over 90 per cent of translation work is in the area of practical translation. Computer-aided translation is ideal for the translation of repetitive practical writings. Translators do not have to translate the sentences they have translated before. The more they translate, the less they have to translate. Computer-aided translation therefore reduces the amount a translator needs to translate by eliminating duplicate work. Some systems, such as Across, allow the user to automatically reuse existing translations from the trans lation memory. It can be seen that ‘reduce, reuse, recycle’ are the three effective ways of increasing profitability (de Ilarraza, Mayor and Sarasola 2000). S. Chan Computer-aided translation45 Professional competence to increase productivity Translators have to work with the help of translation technology. The use of computer-aided translation tools has actually been extended to almost every type of translation work. Computer- aided translation tools are aimed at supporting translators and not at replacing them. They make sure that translation quality is maintained as ‘all output is human input’. As far as the use of tools is concerned, professional translation is technological. In the past, translators used only printed dictionaries and references. Nowadays, translators use electronic concordancers, speech technology, online terminology systems, and automatic checkers. Translation is about the use of a workbench or workstation in translation work. Translation competence or knowledge and skills in languages are not enough today. It is more realistic to talk about professional competence, which includes linguistic competence, cultural competence, translation competence, translator competence, and technological competence. Professional competence is important for translators as it affects their career development. A remark made by Timothy Hunt is worth noting: ‘Computers will never replace translators, but translators who use computers will replace translators who don’t’ (Sofer 2009: 88). What has happened in the field of translation technology shows that Hunt’s remark may not be far off the mark. In the 1980s, very few people had any ideas about translation technology or computer-aided translation. Now, SDL alone has more than 180,000 computer- aided translators. The total number of computer-aided translators in the world is likely to be several times higher than the SDL translators. Profit-seeking to increase productivity Translation is in part vocational, in part academic. In the training of translators, there are courses on translation skills to foster their professionalism, and there are courses on translation theories to enhance their academic knowledge. But there are very few courses on translation as a business or as an industry. It should be noted that translation in recent decades has increasingly become a field of entrepreneurial humanities as a result of the creation of the project management function in computer-aided translation systems. This means translation is now a field of humanities which is entrepreneurial in nature. Translation as a commercial activity has to increase productivity to make more profits. Labour-saving to increase productivity Computer-aided translation systems help to increase productivity and profits through labour- saving, eliminating repetitive translation tasks. Through reusing past translations, an enormous amount of labour is saved. Computer-aided translation tools support translators by freeing them from boring work and letting them concentrate on what they can do best over machines, i.e. handling semantics and pragmatics. Generally, this leads to a broader acceptance by translators. The role of a translator, therefore, has changed drastically in the modern age of digital communication. Rather than simply translating the document, a computer-aided translator has to engage in other types of work, such as authoring, pre-editing, interactive editing, post-editing, term database management, translation memory database management, text alignment and manual alignment verification. It is estimated that with the use of translation technology, the work that was originally borne by six translators can be taken up by just one. 46 Cost-saving to increase productivity Computer-aided translation is also cost-saving. It helps to keep the overhead cost down as what has been translated needs not to be translated again. It helps to improve budget planning. Other issues relating to cost should also be taken into account. First, the actual cost of the tool and its periodic upgrades. Second, the licensing policy of the system, which is about the ease of transferring licences between computers or servers, the incurring of extra charges for client licences, the lending of licences to one’s vendors, freelances, and the eligibility for free upgrades. Third, the cost that is required for support, maintenance, or training. Fourth, the affordability of the system for one’s translators. Fifth, the user-friendliness of the system to one’s computer technicians and translators, which affects the cost of production. Compatibility The concept of compatibility in translation technology must be considered in terms of file formats, operating systems, intersystem formats, translation memory databases, terminology databases, and the languages supported by different systems. Compatibility of file formats One of the most important concepts in translation technology is the type of data that needs to be processed, which is indicated by its format, being shown by one or several letters at the end of a filename. Filename extensions usually follow a period (dot) and indicate the type of information stored in the file. A look at some of the common file types and their file extensions shows that in translation technology, text translation is but one type of data processing, though it is the most popular one. There are two major types of formats: general documentation types and software development types. (I) General documentation types (1) Text files All computer-aided translation systems which use Microsoft Word as text editor can process all formats recognized by Microsoft Word. Throughout the development of translation technology, most computer-aided translation systems process text files (.txt). For Microsoft Word 2000−2003, text files were saved and stored as .doc (document text file/word processing file); for Microsoft Word 2007−2011, documents were saved and stored as .docx (Document text file (Microsoft Office 2007)), .dotx (Microsoft Word 2007 Document Template). Other types of text files include .txt (Text files), .txml (WordFast files), and .rtf (Rich Text files). All automatic and interactive translation systems can process text files, provided the text processing system has been installed in the computer before processing begins. Some of the computer-aided translation systems which can only translate text files include: Across, AidTransStudio, Anaphraseus (formerly known as OpenWordfast), AnyMem (.docx or higher), Araya, Autshumato Integrated Translation Environment (ITE), CafeTran, Déjà Vu, Esperantilo, Fluency, Fusion, OmegaT, Wordfast, and WordFisher. Computer-aided translation systems which can translate text files as well as other formats include CafeTran, Esperantilo, Felix, Fortis, GlobalSight, Google Translator Toolkit, Heartsome Translation Suite, Huajian IAT, Lingo, Lingotek, MadCap Lingo, MemoQ, MemOrg, MemSource, MetaTexis, MultiTrans, S. Chan Computer-aided translation47 OmegaT+, Pootle, SDL-Trados, Similis, Snowman, Swordfish, TM-database, Transit, Wordfast, XTM, and Yaxin. (2) Web-page files HyperText Markup Language (HTML) is a markup language that web browsers use to interpret and compose text, images and other material into visual or audible web pages. HTML defines the structure and layout of a web page or document by using a variety of tags and attributes. HTML documents are stored as .asp (Active Server Pages), .aspx (Active Server Page Extended), .htm (Hypertext Markup Language), .html (Hypertext Markup Language Files), .php (originally: Personal Home Page; now: Hypertext Preprocessor), .jsp (JavaServer Pages), .sgml (Standard Generalized Markup Language File), .xml (Extensible Markup Language file), .xsl (Extensible Stylesheet Language file) files format, which were available since late 1991. Due to the popularity of web pages, web translation has been an important part of automatic and interactive translation systems. Many systems provide comprehensive support for the localization of HTML-based document types. Web page localization is interchangeable with web translation or web localization. Systems that handle HTML include Across, AidTransStudio, Alchemy Publisher, Araya, Atlas, CafeTran, CatsCradle, Déjà Vu, Felix, Fluency, Fortis, GlobalSight, Google Translator Toolkit, Heartsome Translation Suite, Huajian IAT, Lingo, Lingotek, LogiTerm, MemoQ, MemOrg, MetaTexis, MultiTrans, Okapi Framework, OmegaT, OmegaT+, Open Language Tools, Pootle, SDL-Trados, Similis, Snowman, Swordfish, TM-database, TransSearch, Transit, Transolution, and XTM. (3) PDF files Portable Document Format (PDF) (.pdf ) is a universally accepted file interchange format developed by Adobe in the 1990s. The software that allows document files to be transferred between different types of computers is Adobe Acrobat. A PDF file can be opened by the document format, which might require editing to make the file look more like the original, or can be converted to an rtf file for data processing by a computer-aided translation system. Systems that can translate Adobe PDF files and save them as Microsoft Word documents include Alchemy Publisher, CafeTran, Lingo, Similis, and Snowman. (4) Microsoft Office PowerPoint files Microsoft PowerPoint is a presentation program developed to enable users to create anything from basic slide shows to complex presentations, which are comprised of slides that may contain text, images, and other media. Versions of Microsoft Office PowerPoint include Microsoft PowerPoint 2000–2003, .ppt (General file extension), .pps (PowerPoint Slideshow), .pot (PowerPoint template); Microsoft PowerPoint 2007/2011, which are saved as .pptx (Microsoft PowerPoint Open XML Document), .ppsx (PowerPoint Open XML Slide Show), .potx (PowerPoint Open XML Presentation Template), and .ppsm (PowerPoint 2007 Macro- enabled Slide Show). Systems that can handle Powerpoint files include Across, AidTransStudio, Alchemy Publisher, CafeTran, Déjà Vu, Felix, Fluency, Fusion, GlobalSight, Lingotek, LogiTerm, MadCap Lingo, MemoQ, MemSource, MetaTexis, SDL-Trados, Swordfish, TM-database, Transit, Wordfast, XTM, and Yaxin. 48 (5) Microsoft Excel files Different versions of Microsoft Excel include Microsoft Excel 2000–2003 .xls (spreadsheet), .xlt (template); Microsoft Excel 2007: .xlsx (Microsoft Excel Open XML Document), .xltx (Excel 2007 spreadsheet template), .xlsm (Excel 2007 macro-enabled spreadsheet) The computer-aided translation systems that can translate Excel files include Across, AidTransStudio, Déjà Vu, Felix, GlobalSight, Lingotek, LogiTerm, and MemoQ, MemOrg, MetaTexis, MultiTrans, Snowman, Wordfast, and Yaxin. (6) Microsoft Access files One of the computer-aided translation systems which can handle Access with .accdb (Access 2007–2010) file extension is Déjà Vu. (7) Image files The processing of image data, mainly graphics and pictures, is important in computer-aided translation. The data is stored as .bmp (bitmap image file), .jpg (Joint Photographic Experts Group), and .gif (Graphics Interchange Format). One of the computer-aided translation systems that is capable of handling images is CafeTran. (8) Subtitle files One of the most popular subtitle files on the market is .srt (SubRip Text). OmegaT is one of the computer-aided systems that supports subtitle files. (9) Adobe InDesign files Adobe InDesign is desktop publishing software. It can be translated without the need of any third party software by Alchemy Publisher and AnyMem. For Alchemy Publisher, the .indd file must be exported to an .inx format before it can be processed. Other computer-aided translation systems that support Adobe Indesign files include Across, Déjà Vu, Fortis, GlobalSight, Heartsome Translation Suite, Okapi Framework, MemoQ, MultiTrans, SDL-Trados, Swordfish, Transit, and XTM. (10) Adobe FrameMaker Files Adobe FrameMaker is an authoring and publishing solution for XML. FrameMaker files, .fm, .mif and .book, can be opened directly by a system if it is installed with Adobe FrameMaker. Computer-aided translation systems that can translate Adobe FrameMaker files include Across, Alchemy Publisher (which requires a PPF created by Adobe FrameMaker before translating it. Alchemy Publisher supports FrameMaker 5.0, 6.0, 7.0, 8.0, 9.0, FrameBuilder 4.0, and FrameMaker + sgml), CafeTran, Déjà Vu, Fortis, GlobalSight, Heartsome Translation Suite, Lingo, Lingotek, MadCap Lingo, MemoQ, MetaTexis, MultiTrans, SDL-Trados, Swordfish, Transit, Wordfast, and XTM. (11) Adobe PageMaker files Systems that support Adobe PageMaker 6.5 and 7 files include Déjà Vu, GlobalSight, MetaTexis, and Transit. (12) AutoCAD files AutoCAD, developed and first released by Autodesk, Inc. in December 1982, is a software application for computer-aided design (CAD) and drafting which supports both 2D and 3D S. Chan Computer-aided translation49 formats. This software is now used internationally as the most popular drafting tool for a range of industries, most commonly in architecture and engineering. Computer-aided translation systems that support AutoCad are CafeTran, Transit, and TranslateCAD. (13) DTP tagged text files DTP stands for Desktop Publishing. A popular desktop publishing system is QuarkXPress. Systems that support desktop publishing include Across, CafeTran, Déjà Vu, Fortis, GlobalSight, MetaTexis, MultiTrans, SDL-Trados, and Transit. (14) Localization files Localization files include files with the standardized format for localization .xliff (XML Localization Interchange File Format) files, .ttx (XML font file format) files, and .po (Portable Object). Computer-aided translation systems which process XLIFF files include Across Language Server, Araya, CafeTran, Esperantilo, Fluency, Fortis, GTranslator, Heartsome Translation Suite, MadCap Lingo, Lingotek, MemoQ, Okapi Framework, Open Language Tools, Poedit, Pootle, Swordfish, Transolution, Virtaal, and XTM. (II) Software development types (1) Java Properties files Java Properties files are simple text files that are used in Java applications. The file extension of Java Properties file is .properties. Computer-aided translation systems that support Java Properties File include Déjà Vu, Fortis, Heartsome Translation Suite, Lingotek, Okapi Framework, OmegaT+, Open Language Tools, Pootle, Swordfish, and XTM. (2) OpenOffice.org/StarOffice StarOffice of the Star Division was a German company that ran from 1984 to 1999. It was succeeded by OpenOffice.org, an open-sourced version of StarOffice owned by Sun Microsystems (1999–2009) and by Oracle Corporation (2010–2011), which ran from 1999−2011.Currently it is Apache OpenOffice. The format of OpenOffice is .odf (Open Document Format). Computer-aided translation systems which process this type of file include AidTransStudio, Anaphraseus, CafeTran, Déjà Vu, Heartsome Translation Suite, Lingotek, OmegaT, OmegaT+, Open Language Tools, Pootle, Similis, Swordfish, Transolution, and XTM. (3) Windows resource files These are simple script files containing startup instructions for an application program, usually a text file containing commands that are compiled into binary files such as .exe and .dll. File extensions include .rc (Record Columnar File), .resx (NET XML Resource Template). Computer-aided translation systems that process this type of files include Across, Déjà Vu, Fortis, Lingotek, MetaTexis, and Okapi Framework. 50 Compatibility of operating systems One of the most important factors which determined the course of development of computer- aided translation systems is their compatibility with the current operating systems on the market. It is therefore essential to examine the major operating systems running from the beginning of computer-aided translation in 1988 to the present, which include, among others, the Windows of Microsoft and the OS of Macintosh. Microsoft Operating Systems In the world of computing, Microsoft Windows has been the dominant operating system. From the 1981 to the 1995, the x86-based MS-DOS (Microsoft Disk Operating System) was the most commonly used system, especially for IBM PC compatible personal computers. Trados’s Translator’s Workbench II, developed in 1992, is a typical example of a computer- aided translation system working on DOS.DOS was supplemented by Microsoft Windows 1.0, a 16-bit graphical operating environment, released on 20 November 1985 (Windows 2012). In November 1987, Windows 1.0 was succeeded by Windows 2.0, which existed till 2001. Déjà Vu 1.0, released in 1993, was one of the systems compatible with Windows 2.0. Windows 2.0 was supplemented by Windows 286 and Windows 386. Then came Windows 3.0, succeeding Windows 2.1x. Windows 3.0, with a graphical environment, is the third major release of Microsoft Windows, and was released on 22 May 1990. With a significantly revamped user interface and technical improvements, Windows 3 became the first widely successful version of Windows and a rival to Apple Macintosh and the Commodore Amiga on the GUI front. It was followed by Windows 3.1x. During its lifespan from 1992−2001, Windows 3.1x introduced various enhancements to the still MS-DOS- based platform, including improved system stability, expanded support for multimedia, TrueType fonts, and workgroup networking. Trados’s Translator’s Workbench, released in 1994, was a system that was adaptable to Windows 3.1x. Except for Windows and DOS, OS/2 is also one of the operation systems that support computer-aided translation systems, especially in late 1980s and early 1990s. Apple Operating Systems Mac OS (1984−2000) and OS X (2001−) are two series of graphical user interface-based operating systems developed by Apple Inc. for their Macintosh line of computer systems. Mac OS was first introduced in 1984 with the original Macintosh and this series was ended in 2000. OS X, first released in March 2001, is a series of Unix-based graphical interface operating systems. Both series share a general interface design, but have very different internal architectures. Only one computer-aided translation system, AppleTrans, is designed for OS X. Its initial released was announced in February 2004 and the latest updated version was version 1.2(v38) released in September 2006, which runs on Mac OS X 10.3 or later. Another computer-aided translation system, Wordfast Classic was released to upgrade its support of the latest text processor running on Mac OS X, such as Wordfast Classic 6.0, which is compatible for MS Word 2011 for Mac. Other computer-aided translation systems that can run on Mac OS or OS X are cross- platform software, rather than software developed particularly for Mac. Examples are Java- based applications, such as Autshumato, Heartsome, OmegaT, Open Language Tools and S. Chan Computer-aided translation51 Swordfish. Besides, all cloud-based systems can support Mac OS and OS X, including Wordbee, XTM Cloud, Google Translator’s Toolkit, Lingotek Collaborative Translation Platform, MemSource Cloud, and WebWordSystem.OS/2 is a series of computer operating systems, initially created by Microsoft and IBM, then later developed by IBM exclusively. The name stands for ‘Operating System/2’. Until 1992, the early computer-aided translation systems ran either on MS-DOS or OS/2. For example, IBM Translation Manager/2 (TM/2) was released in 1992 and run on OS/2. ALPS’s translation tool also ran on OS/2. But OS/2 had a much smaller market share compared with Windows in early 1990s. Computer-aided translation system developers therefore gradually shifted from OS/2 and MS-DOS to Windows or discontinued the development of OS/2 and MS-DOS compatible computer-aided translation systems. By the end of the 1990s, most computer-aided translation systems mainly ran on Windows, although some developers offered operating-system customization services upon request. OS/2 4.52 was released in December 2001. IBM ended its support to OS/2 on 31 December 2006. Compatibility of databases Compatibility of translation memory databases TMX (Translation Memory eXchange), created in 1998, is widely used as an interchange format between different translation memory formats. TMX files are XML (eXtensible Markup Language) files whose format was originally developed and maintained by OSCAR (Open Standards for Container/Content Allowing Re-use) of the Localization Industry Standards Association. The latest official version of the TMX specification, version 1.4b, was released in 2005. In March 2011 LISA was declared insolvent; as a result its standards were moved under the Creative Commons licence and the standards specification relocated. The technical specification and a sample document of TMX can be found on the website of The Globalization and Localization Association. TMX has been widely adopted and is supported by more than half of the current computer- aided translation systems on the market. The total number of computer-aided translation systems that can import and export translation memories in TMX format is 54, including Across, Alchemy Publisher, Anaphraseus, AnyMem, Araya, ATLAS, Autshumato, CafeTran, Crowdin, Déjà Vu, EsperantiloTM, Felix, Fluency, Fortis, Fusion, GE-CCT, GlobalSight, Google Translator Toolkit, Heartsome, Huajian IAT, Lingotek, LogiTerm, LongRay CAT, MadCap Lingo, MemoQ, MemSource, MetaTexis, MT2007, MultiTrans, OmegaT, OmegaT+, Open Language Tools, OpenTM2, OpenTMS, PROMT, SDL Trados, Snowball, Snowman, Swordfish, Systran, Text United, The Hongyahu, TM Database, Transit, Translation Workspace, Transwhiz, TraTool, Webwordsystem, Wordbee Translator, Wordfast Classic and Wordfast Pro, XTM, Yaxin CAT, and 翻訳ブレイン (Translation Brain). Compatibility of terminology databases Compatibility of terminology databases is best illustrated by TermBase eXchange (TBX), which covers a family of formats for representing the information in a high-end termbase in a neutral intermediate format in a manner compliant with the Terminological Markup Framework (TMF) (Melby 2012: 19−21). Termbase Exchange is an international standard as well as an industry standard. The industry standard version differs from the ISO standard only by having different title pages. Localization 52 Industry Standards Association, the host organization for OSCAR that developed Termbase Exchange, was dissolved in February 2011. In September 2011, the European Telecommunications Standards Institute (ETSI) took over maintenance of the OSCAR standards. ETSI has established an interest group for translation/localization standards and a liaison relationship with the International Organization for Standardization (ISO) so that TBX can continue to be published as both an ISO standard and an industry standard. There are many types of termbases in use, ranging from huge termbases operated by governments, to medium-size termbases maintained by corporations and non-governmental organizations, to smaller termbases maintained by translation service providers and individual translators. The problem addressed by the designers of term exchange was that existing termbases are generally not interoperable. They are based on different data models that use a variety of data categories. And even if the same data category is used for a particular piece of information, the name of the data category and the values allowed for the data category may be different. Compatibility of rules segmentation rules exchange Segmentation Rules eXchange (SRX) is an XML-based standard that was maintained by Localization Industry Standards Association, until it became insolvent in 2011 and then this standard is now maintained by the Globalization and Localization Association (GALA). Segmentation Rules eXchange provides a common way to describe how to segment text for translation and other language-related processes. It was created when it was realized that translation memory exchange leverage is lower than expected in certain instances due to differences in how tools segment text. Segmentation Rules eXchange is intended to enhance the translation memory exchange so that translation memory data that is exchanged between applications can be used more effectively. Having the segmentation rules that were used when a translation memory was created will increase the leverage that can be achieved when deploying the translation memory data. Compatibility with the languages supported As computer-aided translation systems cannot identify languages, language compatibility is therefore an important concept in translation technology. There are a large number of languages and sub-languages in the world, totalling 6,912. But the number of major languages computers can process is relatively small. It is therefore important to know whether the languages that require machine processing are supported by a system or not. With the aid of unicode, most of the languages in the world are supported in popular computer-aided translation systems. Unicode is a computing industry standard for the consistent encoding, representation and handling of text expressed in most of the world’s writing systems. There are basically two major types of language and sub-language codes. Some systems, such as OmegaT and XTM, use letters for language codes (2 or 3 letters) and language-and-region codes (2+2 letters), which can be selected from a drop-down list. OmegaT follows the ISO 639 Code Tables in preparing its code list. French for example, is coded fr with the language- and region code for French (Canada) as fr-CA. The following is a list of languages supported by Wordfast Classics and XTM, two of the nine computer-aided translation systems chosen for analysis in this chapter. S. Chan Computer-aided translation53 wordfast classic Wordfast can be used to translate any of the languages supported by Microsoft Word. The number of languages supported by Microsoft is 91, with a number of sub-languages for some major languages. [Afro-Asiatic] Arabic (Algeria), Arabic (Bahrain), Arabic (Egypt), Arabic (Iraq), Arabic (Jordan), Arabic (Kuwait), Arabic (Lebanon), Arabic (Libya), Arabic (Morocco), Arabic (Oman), Arabic (Qatar), Arabic (Saudi Arabia), Arabic (Syria), Arabic (Tunisia), Arabic (U.A.E.), Arabic (Yemen), Hebrew, Maltese [Altaic] Azeri (Cyrillic), Azeri (Latin), Japanese, Korean, Turkish [Austro-Asiatic] Vietnamese [Austronesian] Indonesian, Malay (Brunei Darussalam), Malaysian [Basque] Basque [Dravidian] Kannada, Malayalam, Tamil, Telugu [Indo-European] Afrikaans, Albanian, Armenian, Assamese, Belarusian, Bengali, Bulgarian, Byelorussian, Catalan, Croatian, Czech, Danish, Dutch, Dutch (Belgian), English (Australia), English (Belize), English (Canadian), English (Caribbean), English (Ireland), English (Jamaica), English (New Zealand), English (Philippines), English (South Africa), English (Trinidad), English (U.K.), English (U.S.), English (Zimbabwe), Faroese, Farsi, French (Belgian), French (Cameroon), French (Canadian), French (Congo), French (Cote d’Ivoire), French (Luxembourg), French (Mali), French (Monaco), French (Reunion), French (Senegal), French (West Indies), Frisian (Netherlands), Gaelic (Ireland), Gaelic (Scotland), Galician, German, German (Austria), German (Liechtenstein), German (Luxembourg), Greek, Gujarati, Hindi, Icelandic, Italian, Kashmiri, Konkani, Latvian, Lithuanian, Macedonian (FYRO), Marathi, Nepali, Norwegian (Bokmol), Norwegian (Nynorsk), Oriya, Polish, Portuguese, Portuguese (Brazil), Punjabi, Rhaeto-Romance, Romanian, Romanian (Moldova), Russian, Russian (Moldova), Sanskrit, Serbian (Cyrillic), Serbian (Latin), Sindhi, Slovak, Slovenian, Sorbian, Spanish (Argentina), Spanish (Bolivia), Spanish (Chile), Spanish (Colombia), Spanish (Costa Rica), Spanish (Dominican Republic), Spanish (Ecuador), Spanish (El Salvador), Spanish (Guatemala), Spanish (Honduras), Spanish (Nicaragua), Spanish (Panama), Spanish (Paraguay), Spanish (Peru), Spanish (Puerto Rico), Spanish (Spain), Spanish (Traditional), Spanish (Uruguay), Spanish (Venezuela), Swedish, Swedish (Finland), Swiss (French), Swiss (German), Swiss (Italian), Tajik, Ukrainian, Urdu, Welsh [Kartvelian] Georgian [Niger-Congo] Sesotho, Swahili, Tsonga, Tswana, Venda, Xhosa, Zulu [Sino-Tibetan] Burmese, Chinese, Chinese (Hong Kong SAR), Chinese (Macau SAR), Chinese (Simplified), Chinese (Singapore), Chinese (Traditional), Manipuri, Tibetan 54 [Tai-Kadai] Laothian, Thai [Turkic] Tatar, Turkmen, Uzbek (Cyrillic), Uzbek (Latin) [Uralic] Estonian, Finnish, Hungarian, Sami Lappish xtm The languages available in XTM are 157, not including varieties within a single language. These languages are as follows: [Afro-Asiatic] Afar, Amharic, Arabic, Hausa, Hebrew, Maltese, Oromo, Somali, Sudanese Arabic, Syriac, Tigrinya, [Altaic] Azeri, Japanese, Kazakh, Korean, Mongolian, Turkish [Austro-Asiatic] Khmer, Vietnamese [Austronesian] Fijian, Indonesian, Javanese, Malagasy, Malay, Maori, Nauru, Samoan, Tagalog, Tetum, Tonga [Aymaran] Aymara [Bantu] Kikongo [Basque] Basque [Constructed Language] Esperanto, Interlingua, Volapk [Dravidian] Kannada, Malayalam, Tamil, Telugu [English Creole] Bislama [Eskimo-Aleut] Greenlandic, Inuktitut, Inupiak [French Creole] Haitian Creole [Hmong-Mien] Hmong [Indo-European] Afrikaans, Armenian, Assamese, Asturian, Bengali, Bihari, Bosnian, Breton, Bulgarian, Byelorussian, Catalan, Corsican, Croatian, Czech, Danish, Dari, Dhivehi, Dutch, English, Faroese, Flemish, French, Frisian, Galician, German, Greek, Gujarati, Hindi, Icelandic, Irish, Italian, Kashmiri, Konkani, Kurdish, Latin, Latvian, Lithuanian, Macedonian, Marathi, Montenegrin, Nepali, Norwegian, Occitan, Oriya, Pashto, Persian, Polish, Portuguese, Punjabi, Rhaeto-Romance, Romanian, Russian, Sanskrit, Sardinian, Scottish Gaelic, Serbian, Sindhi, Singhalese, Slovak, Slovenian, Sorbian, Spanish, Swedish, Tajik, Ukrainian, Urdu, Welsh, Yiddish S. Chan Computer-aided translation55 [Kartvelian] Georgian [Ngbandi-based Creole] Sango [Niger-Congo] Chichewa, Fula, Igbo, Kinyarwanda, Kirundi, Kiswahili, Lingala, Ndebele, Northern Sotho, Sesotho, Setswana, Shona, Siswati, Tsonga, Tswana, Twi, Wolof, Xhosa, Yoruba, Zulu [Northwest Caucasian] Abkhazian [Quechuan] Quechua [Romanian] Moldavian [Sino-Tibetan] Bhutani, Burmese; Chinese, Tibetan [Tai-Kadai] Laothian, Thai [Tupi] Guarani [Turkic] Bashkir, Kirghiz, Tarar, Turkmen, Uyghur, Uzbek [Uralic] Estonian, Finnish, Hungarian Several observations can be made from the languages supported by the current eleven systems.(1) The number of languages supported by language-specific systems is small as they need to be supplied with language-specific dictionaries to function well. Yaxin is best for English− Chinese translation, covering two languages, while most non-language-specific systems support around or above 100 languages. (2) For the seven systems developed in Europe, the United Kingdom, and the United States, which include Across, Déjà Vu, MemoQ, OmegaT, SDL Trados, Wordfast, and XTM, the Indo-European languages take up around 51.89 per cent, while the proportion of the non- Indo-European languages is 48.11 per cent. Table 2.1 shows the details: Table 2.1 Statistics of languages supported by 7 CAT systems Name of the system Number of languages supported Number of language families supported Number and percentage of Indo-European languages Number and percentage of non-Indo-European languages Across 121 1861 (50.41%) 60 (49.59%) Déjà Vu 132 2166 (50%) 66 (50%) MemoQ 102 1654 (52.94%) 48 (47.06%) OmegaT 90 1448 (53.33%) 42 (46.67%) SDL Trados 115 1862 (53.91%) 53 (46.09%) Wordfast 91 1354 (59.34%) 37 (40.66%) XTM 157 2668 (43.31%) 89 (56.69%) 56 Controllability One of the main differences between human translation and computer-aided translation lies in the degree of control over the source text. In human translation, there is no need, or rather it is not the common practice, to control how and what the author should write. But in computer-aided translation, control over the input text may not be inappropriate as the output of an unedited or uncontrolled source language text is far from satisfactory (Adriaens and Macken 1995: 123−141; Allen and Hogan 2000: 62−71; Arnold et al. 1994; Hurst 1997: 59−70; Lehtola, Tenni and Bounsaythip 1998: 16−29; Mitamura 1999: 46−52; Murphy et al. 1998; Nyberg et al. 2003: 245−281; Ruffino 1985: 157−162). The concept of controllability is realized in computer-aided translation by the use of controlled language and the method of pre-editing. Controllability by the use of controlled language An effective means of achieving controllability in translation technology is controlled language (see Figure 2.16). The idea of controlled language was created, partly at least, as a result of the problems with natural languages which are full of complexities, ambiguities, and robustness (Nyberg et al. 2003: 245−281). A strong rationale for controlled language is that a varied source text generates a poor target text, while a controlled source text produces a quality target text. (Bernth 1999). Controlled language is therefore considered necessary (Caeyers 1997: 91−103; Hu 2005: 364−372). Controlled language, in brief, refers to a type of natural language developed for specific domains with a clearly defined restriction on controlled lexicons, simplified grammars, and style rules to reduce the ambiguity and complexity of a text so as to make it easier to be understood by users and non-native speakers and processed by machine translation systems (Chan 2004: 44; Lux and Dauphin 1996: 193−204). Control over the three stages of a translation procedure, which include the stage of inputting a source text, the stage of transfer, and the stage of text generation, is generally regarded as a safe guarantee of quality translation. Control of the source text is in the form of controlled authoring, which makes the source text easier for computer processing (Allen 1999; Chan 2004: 44; van der Eijk and van Wees 1998: 65−70; Zydron 2003). The text produced is a ‘controlled language text’ (Melby 1995: 1). There is also control over the transfer stage. And the output of a machine translation system is known as ‘controlled translation’ (Carl 2003: 16−24; Gough and Way 2004: 73−81; Rico and Torrejon 2004; Roturier 2004; Torrejón 2002: 107−116), which is alternatively known as a ‘controlled target language text’ (Chan 2004: 44). In short, a controlled text is easier to be processed by machine translation systems to produce a quality output. Goals and means of controlled language Controlled language is used by both humans and computers. The goals of controlled language are to make the source text easier to read and understand. These goals are to be achieved at the lexical and sentential levels. At the lexical level, controlled language is about the removal of lexical ambiguity and the reduction in homonymy, synonymy, and complexity. This is to be achieved by one-to-one correspondence in the use and translation of words, known as one-word one-meaning. An example is to use only the word ‘start’ but not similar words such as ‘begin’, ‘commence’, S. Chan Computer-aided translation57 Figure 2.16 Controlled language ‘initiate’, and ‘originate’. The second method is to use the preferred language, such as American English but not British English. The third method is to have a limited basic vocabulary (Bjarnestam 2003; Chen and Wu 1999; Probst and Levin 2002: 157−167; Wasson 2000: 276−281), which can be illustrated by the use of a controlled vocabulary of 3,100 words in aircraft-maintenance documentation at the European Association of Aerospace Industries (AECMA) in 1980 (AECMA 1995).At the sentential level, controlled language is about the removal of syntactical ambiguity, the simplification of sentence structures, limitations on sentence length, and constraints on voice, tense, and other grammatical units. To do all these, there are a limited number of strictly stipulated writing rules to follow. The European Association of Aerospace Industries had 57 writing rules. Short sentences are preferred over long and complex sentences. And there is also a limit on the number of words in a sentence. For procedural text, there should be no more than twenty words. For descriptive texts, the number is twenty-five. There are also rules governing grammatical well-formedness (Loong 1989: 281−297), restricted syntax, and the use of passive construction in procedural texts. At the suprasentential level, there is a limit of six sentences in a paragraph, the maximum number of clauses in a sentence, and the use of separate sentences for sequential steps in procedural texts. This means setting limits on the length of a sentence, such as setting the number of words at twenty, using only the active voice, and expressing one instruction or idea by one sentence. Controlled language checkers Controlled language cannot be maintained manually; it relies on the use of different kinds of checkers, which are systems to ensure that a text conforms to the rules of a particular controlled language (Fouvry and Balkan 1996: 179−192). There is the automatic rewriting system, which is specially developed for controlled language, rewriting texts automatically into controlled language without changing the meaning of the sentences in the original in order to produce a high-quality machine translation. There is the controlled language checker, which is software that helps an author to determine whether a text conforms to the approved words and writing rules of a particular controlled language. Checkers can also be divided into two types: in-house controlled language checker and commercial controlled language checker. In-house controlled language checkers include the 58 PACE (Perkins Approved Clear English) of Perkins Engines Ltd, the Controlled English of Alcatel Telecom, and the Boeing Simplified English Checker of the Boeing Company (Wojcik and Holmback 1996: 22−31). For commercial controlled language checkers, there are a number of popular systems. The LANTmaster Controlled Checker, for example, is a controlled language checker developed by LANT in Belgium. It is based on work done for the METAL (Mechanical Translation and Analysis of Languages) machine translation project. It is also based on the experience of the Simplified English Grammar and Style Checker (SECC) project (Adriaens 1994: 78–88; Adriaens and Macken 1995: 123−141). The MAXit Checker is another controlled language software developed by Smart Communications Incorporation to analyse technical texts written in controlled or simplified English with the use of more than 8,500 grammar rules and artificial intelligence to check the clarity, consistency, simplicity, and global acceptance of the texts. The Carnegie Group also produced the ClearCheck, which performs syntactic parsing to detect such grammatical problems as ambiguity (Andersen 1994: 227). Advantages and disadvantages of controlled language The advantages of controlled language translation are numerous, including high readability, better comprehensibility, greater standardization, easier computer processing, greater reusability, increased translatability, improved consistency, improved customer satisfaction, improved competitiveness, greater cost reduction in global product support, and enhanced communication in global management.There are a number of disadvantages in using controlled language, such as expensive system construction, high maintenance cost, time-consuming authoring, and restrictive checking process. Controlled language in use As the advantages of using controlled language outweigh its disadvantages, companies started to use controlled language as early as the 1970s. Examples of business corporations which used controlled languages include Caterpillar Fundamental English (CFE) of the Caterpillar Incorporation in 1975 (Kamprath et al. 1998: 51−61; Lockwood 2000: 187−202), Smart Controlled English of the Smart Communications Ltd in 1975, Douglas Aircraft Company in 1979, the European Association of Aerospace Industries (AECMA) in 1980, the KANT Project at the Center for Machine Translation, Carnegie Mellon University in 1989 (Allen 1995; Carbonell et al. 1992: 225−235; Mitamura et al. 1994: 232−233; Mitamura and Nyberg 1995: 158−172; Mitamura et al. 2002: 244−247; Nyberg and Mitamura 1992: 1069−1073; Nyberg et al. 1997; Nyberg et al. 1998: 1−7; Nyberg and Mitamura 2000: 192−195), the PACE of Perkins Engines Ltd. in 1989, ScaniaSwedish in Sweden in 1995 (Almqvist and Hein 1996: 159−164; Hein 1997), General Motors in 1996, Ericsson English in Sweden in 2000, Nortel Standard English in the United Kingdom in 2002, and Oce Technologies English in Holland in 2002. Controlled language in computer-aided translation systems The concept of controlled language is realized in controlled authoring in computer-aided translation systems. Authoring checking tools are used to check and improve the quality of the source text. There is an automatic rewriting system which is usually used as a tool to realize controlled authoring. One of the computer-aided translation systems that performs controlled S. Chan Computer-aided translation59 authoring is Star Transit. This system provides automatic translation suggestions from the translation memory database from a speedy search engine and it is an open system that can integrate with many authoring systems. Customizability Customizability, etymologically speaking, is the ability to be customized. More specifically, it refers to the ability of a computer or computer-aided translation system to adapt itself to the needs of the user. Customizing a general-purpose machine translation system is an effective way to improve MT quality. Editorial customization Pre-editing is in essence a process of customization. The customization of machine translation systems, which is a much neglected area, is necessary and essential as most software on the market are for general uses and not for specific domains. Practically, system customization can be taken as part of the work of pre-editing as we pre-edit the words and expressions to facilitate the production of quality translation.The degree of customization depends on the goals of translation, and the circumstances and the type of text to be translated. Language customization It is true that there are many language combinations in computer-aided translation systems to allow the user to choose any pair of source and target languages when creating a project, yet many users only work with a limited set of source and target languages. XTM, a cloud-based system, allows the user to set language combinations through the Data section. In the language combinations section, the project administrator or user can reduce and customize the available languages to be used, set the language combinations for the entire system and set specific language combinations for individual customers (XTM International 2012: 15). Language customization in XTM, for example, can be conducted on the Customize tab where there are three options for the user to modify and use language combinations. The first option is ‘system default language combinations’, which is the full set of unmodified language combinations. The second option is ‘system defaults with customized language combinations’, which is the full set of language combinations in which the user may have customized some parameters. The third option is ‘customized language combinations only’, which include only the language combinations that the user has customized. It is possible to add or delete the source and target languages in the selected customized option. Lexicographical customization Lexicographical customization is best shown in the creation of custom dictionaries for each customer, other than the dictionaries for spell checking. This means that multiple translators working on projects for the same customer will use the same custom dictionary. 60 Linguistic customization As far as linguistic customization is concerned, there are basically two levels of customization: lexical customization and syntactical customization. Lexical customization Lexical customization is to customize a machine translation system by preparing a customized dictionary, in addition to the system dictionary, before translating. This removes the uncertainties in translating ambiguous words or word combinations. It must be pointed out, however, that the preparation of a customized dictionary is an enormous task, involving a lot of work in database creation, database maintenance, and database management. Syntactical customization Syntactical customization, on the other hand, is to add sentences or phrases to the database to translate texts with many repetitions. Syntactical customization is particularly important when there is a change of location for translation consumption. The translation memory databases built up in Hong Kong for the translation of local materials, for example, may not be suitable for the production of translations targeted at non-Hong Kong readers, such as those in mainland China. Resource customizationWebsite customization Some computer-aided translation systems allow the user to create resource profile settings. Each profile in Fluency, for example, has four customized uniform resource locators (URLs) associated with it. URLs are the Internet addresses of information. Each document or file on the Internet has a unique address for its location. Fluency allows the user to have four URLs of one’s preference, two perhaps for specialized sites and two general sites. Machine translation system customization Some systems are connected to installed machine translation systems the terminology databases of which can be customized for the generation of output, thus achieving terminological consistency in the target text. Collaborativity Collaborativity is about continuously working and communicating with all parties relating to a translation project, from the client to the reviewer, in a shared work environment to generate the best benefits of team work. Computer-aided translation is a modern mode of translation production that works best in team translation. In the past and decreasingly at present, individual translation has been the norm of practice. At present and increasingly in the future, team translation is the standard practice. A number of systems, such as Across and Wordfast, can allow users to interact with each other through the translation memory server and share translation memory assets in real time. S. Chan Computer-aided translation61 Translation is about management. Translation business operates on projects. Translation technology is about project management, about how work is to be completed by translation teams. With the use of translation technology, the progress of translation work is under control and completed with higher efficiency. The best way to illustrate this point is project collaboration, which allows translators and project managers to easily access and distribute projects and easily monitor their progress. The work of translation in the present digital era is done almost entirely online with the help of a machine translation or computer-aided translation system. This can be illustrated with SDL-Trados 2014, which is a computer-aided translation system developed by SDL International and generally considered to be the most popular translation memory system on the market. Figure 2.17 shows the dashboard of SDL-Trados 2014. Figure 2.17 Dashboard of SDL-Trados 2014 Figure 2.18 List of current projects 62 Figure 2.19 Project details Workflow of a translation project To start a project, the first stage of the workflow is the creation of a termbase and a translation memory database, as shown in Figure 2.20. Figure 2.20 Workflow of a translation project: the first stage S. Chan Computer-aided translation63 In other words, when the Project Manager has any publications, files or web pages to translate, he will send them to the translators of a department or unit, or freelancers for processing. They will create translation units and term databases from these pre-translated documents and save these databases in the SDL-Trados 2014 Server. This is the first stage of the workflow. After the creation of translation memory and term databases, as shown in Figure 2.21, the Project Manager can then initiate a translation project and monitor its progress with the use of SDL-Trados 2014 (as indicated by ). He can assign and distribute source files to in-house and / or freelance translators by emails (as indicated by  ). Translators can then do the translation by (i) reusing the translation memories and terms stored in the databases; (ii) adding new words or expressions to the translation memory and term databases (as indicated by ). When the translation is done, translators send their translated files back to the Project Manager on or before the due date (as indicated by ). When the Project Manager receives the translated files, he updates the project status, finalizes the project and marks it as ‘complete’ (as indicated by ). To make sure that SDL-Trados 2014 has a smooth run, a technical support unit to maintain the SDL-Trados server may be necessary (as indicated by ). Figure 2.21 Workflow of a translation project: the second stage A translation team usually consists of the following members. Project manager A project manager is a professional in the field of project management. The responsibilities of a project manager include the following: 1 plan, execute, and close projects (When planning a project, the project manager works on the overall resources and budget of the project. When executing a project, the project manager can add or import customers and subcontract projects.) 2 create clear and attainable project objectives; 3 build the project requirements; and 4 manage cost, time, and scope of projects. 64 Terminologist A terminologist is one who manages terms in the terminology database. There are two types of terminologists: (1) customer-specific terminologists who can only access the terminology of one customer; and (2) global experts who can access all the terms in the systems for all customers. Conclusion This chapter is possibly the first attempt to analyse the concepts that have governed the growth of functionalities in computer-aided translation systems. As computing science and related disciplines advance, more concepts will be introduced and more functions will be developed accordingly. However, it is believed that most of the concepts discussed in this chapter will last for a long time. References Adriaens, Geert (1994) ‘Simplified English Grammar and Style Correction in an MT Framework: The LRE SECC Project’, in Translating and the Computer 16, London: The Association for Information Management, 78−88. Adriaens, Geert and Lieve Macken (1995) ‘Technological Evaluation of a Controlled Language Application: Precision, Recall and Convergence Tests for SECC’, in Proceedings of the 6th International Conference on Theoretical and Methodological Issues in Machine Translation (TMI-95), 5−7 July 1995, University of Leuven, Leuven, Belgium, 123−141. AECMA (1995) ‘A Guide for the Preparation of Aircraft Maintenance Documentation in the International Aerospace Maintenance Language−Issue 1’, Brussels, Belgium. Al-Shabab, Omar Sheikh (1996) Interpretation and the Language of Translation: Creativity and Convention in Translation, London: Janus Publishing Company. Allen, Jeffrey (1995) Review of the Caterpillar KANT English-French MT System, Internal Technical Report, Peoria, IL: Technical Information Department, Caterpillar Inc. Allen, Jeffrey (1999) ‘Adapting the Concept of “Translation Memory” to “Authoring Memory” for a Controlled Language Writing Environment’, in Translating and the Computer 20, London: The Association for Information Management. Allen, Jeffrey and Christopher Hogan (2000) ‘Toward the Development of a Post-editing Module for Raw Machine Translation Output: A Controlled Language Perspective’, in Proceedings of the 3 rd International Workshop on Controlled Language Applications (CLAW 2000), 29−30 April 2000, Seattle, WA, 62−71. Almqvist, Ingrid and Anna Sågvall Hein (1996) ‘Defining ScaniaSwedish − Controlled Language for Truck Maintenance’, in Proceedings of the 1st International Workshop on Controlled Language Applications (CLAW-96), Leuven, Belgium, 159–164. Andersen, Peggy (1994) ‘ClearCheck Demonstration’, in Proceedings of the 1st Conference of the Association for Machine Translation in the Americas: Technology Partnerships for Crossing the Language Barrier (AMTA-1), 5−8 October 1994, Columbia, MD, 227. Arnold, Doug J., Lorna Balkan, R. Lee Humphreys, Seity Meijer, and Louisa Sadler (1994) Machine Translation: An Introductory Guide, Manchester and Oxford: NCC Blackwell. Bell, Roger T. (1991) Translation and Translating: Theory and Practice, London and New York: Longman. Bernth, Arendse (1999) Tools for Improving E-G MT Quality, Yorktown Heights, NY: IBM T.J. Watson Research Center. Bjarnestam, Anna (2003) ‘Internationalizing a Controlled Vocabulary Based Search Engine for Japanese’, in Proceedings of the Localization World Conference 2003, 14−16 October 2003, Seattle, WA. Bly, Robert (1983) The Eight Stages of Translation, Boston, MA: Rowan Tree Press. Caeyers, Herman (1997) ‘Machine Translation and Controlled English’, in Proceedings of the 2nd Workshop of the European Association for Machine Translation: Language Technology in Your Organization? 21−22 May 1997, University of Copenhagen, Copenhagen, Denmark, 91−103. S. Chan Computer-aided translation65 Carbonell, Jaime G., Teruko Mitamura, and Eric H. Nyberg (1992) ‘The KANT Perspective: A Critique of Pure Transfer (and Pure Interlingua, Pure Statistics,…)’, in Proceedings of the 4 th International Conference on Theoretical and Methodological Issues in Machine Translation of Natural Languages: Empiricist vs Rationalist Methods in MT (TMI-92), Montreal, Quebec, Canada, 225−235. Carl, Michael (2003) ‘Data-assisted Controlled Translation’, in Proceedings of the Joint Conference Combining the 8 th International Workshop of the European Association for Machine Translation and the 4th Controlled Language Applications Workshop: Controlled Language Translation (EAMT- CLAW-2003), Dublin City University, Ireland, 16−24. Chan, Sin-wai (2004) A Dictionary of Translation Technology, Hong Kong: The Chinese University Press. Chen, Kuang-Hua and Chien-Tin Wu (1999) ‘Automatically Controlled-vocabulary Indexing for Text Retrieval’, in Proceedings of the International Conference on Research in Computational Linguistics (ROCLING-XII), Taipei, Taiwan. de Ilarraza, Arantxa Diaz, Aingeru Mayor, and Kepa Sarasola (2000) ‘Reusability of Wide-coverage Linguistic Resources in the Construction of Multilingual Technical Documentation’, in Proceedings of the International Conference on Machine Translation and Multilingual Applications in the New Millenium (MT- 2000), University of Exeter, England. Delisle, Jean (1988) Translation: An Interpretive Approach, Patricia Logan and Monica Creery (trans.), Ottawa and London: University of Ottawa Press. Fais, Laurel and Kentaro Ogura (2001) ‘Discourse Issues in the Translation of Japanese Email’, in Proceedings of the 5th Pacific Association for Computational Linguistics Conference (PACLING-2001), Fukuoka, Japan. Fouvry, Frederik and Lorna Balkan (1996) ‘Test Suites for Controlled Language Checkers’, in Proceedings of the 1st International Workshop on Controlled Language Applications, Katholieke Universiteit, Leuven, Belgium, 179−192. Gough, Nano and Andy Way (2004) ‘Example-based Controlled Translation’, in Proceedings of the 9th Workshop of the European Association for Machine Translation: Broadening Horizons of Machine Translation and Its Applications, Foundation for International Studies, Malta, 73−81. Han, Benjamin, Donna Gates, and Lori S. Levin (2006) ‘Understanding Temporal Expressions in Emails’, in Proceedings of the Human Language Technology Conference − Annual Meeting of the North American Chapter of the Association for Computational Linguistics (HLT-NAACL-2006), New York. Hein, Anna Sagvall (1997) ‘Scania Swedish-A Basis for Multilingual Translation’, in Translating and the Computer 19, London: The Association for Information Management. http://www.alchemysoftware.ie/index.html. http://www.helicon.co.at/aboutus.html. http://www.internetworldstats.com/stats.htm. http://www.lisa.org/Glossary. http://www2.multilizer.com/company. http://www.passolo.com. http://www.schaudin.com. Hu, Qingping 胡清平 (2005)〈受控語言及其在漢英機器翻譯裏的應用前景〉(Controlled Language and Its Prospective Application in Chinese-English Machine Translation), in Luo Xuanmin 羅選民 (ed.) 《語言認識與翻譯研究》(Language, Cognition and Translation Studies), Beijing: Foreign Language Press 外文出版社, 364−372. Hurst, Matthew F. (1997) ‘Parsing for Targeted Errors in Controlled Languages’, in Ruslan Mitkov and Nicolas Nicolov (eds) Recent Advances in Natural Language Processing, Amsterdam and Philadelphia: John Benjamins, 59−70. Joy, Lorna (2002) ‘Translating Tagged Text − Imperfect Matches and a Good Finished Job’, Translating and the Computer 24, London: The Association for Information Management. Kamprath, Christine, Eric Adolphson, Teruko Mitamura, and Eric H. Nyberg (1998) ‘Controlled Language Multilingual Document Production: Experience with Caterpillar Technical English’, in Proceedings of the 2nd International Workshop on Controlled Language Applications (CLAW-98), Language Technologies Institute, Carnegie Mellon University, Pittsburgh, PA, 51−61. Kay, Martin (1980) ‘The Proper Place of Men and Machines in Language Translation’, Research Report CSL-80-11, Xerox Palo Alto Research Center, Palo Alto, CA. Lehtola, Aarno, Jarno Tenni, and Catherine Bounsaythip (1998) ‘Controlled Language—An Introduction’, in Proceedings of the 2nd International Workshop on Controlled Language Applications, Carnegie Mellon University, Pittsburgh, PA, 16−29. 66 Lockwood, Rose (2000) ‘Machine Translation and Controlled Authoring at Caterpillar’, in Robert C. Sprung (ed.) Translating into Success: Cutting-edge Strategies for Going Multilingual in a Global Age, Amsterdam and Philadelphia: John Benjamins, 187–202. Loong, Cheong Tong (1989) ‘A Data-driven Control Strategy for Grammar Writing Systems’, Machine Translation 4(4): 281–297. Lockwood, Rose (2000) ‘Machine Translation and Controlled Authoring at Caterpillar’, in Robert C. Sprung (ed.) Translating into Success: Cutting-edge Strategies for Going Multilingual in a Global Age, Amsterdam and Philadelphia: John Benjamins, 187−202. Lux, Veronika and Eva Dauphin (1996) ‘Corpus Studies: A Contribution to the Definition of a Controlled Language’, in Proceedings of the 1st International Workshop on Controlled Language Applications (CLAW- 96), Leuven, Belgium, 193−204. Matsuda, Junichi and Hiroyuki Kumai (1999) ‘Transfer-based Japanese-Chinese Translation Implemented on an E-mail System’, in Proceedings of MT Summit VII: MT in the Great Translation Era, 13−17 September 1999, Kent Ridge Digital Labs, Singapore. Melby, Alan K. (1995) The Possibility of Language: A Discussion of the Nature of Language, with Implications for Human and Machine Translation, Amsterdam and Philadelphia: John Benjamins. Melby, Alan K. (2012) ‘Terminology in the Age of Multilingual Corpora’, The Journal of Specialized Translation 18: 7−29. Mitamura, Teruko, Eric H. Nyberg, and Jaime G. Carbonell (1994) ‘KANT: Knowledge-based, Accurate Natural Language Translation’, in Proceedings of the 1st Conference of the Association for Machine Translation in the Americas: Technology Partnerships for Crossing the Language Barrier (AMTA-1), 5−8 October 1994, Columbia, MD, 232−233. Mitamura, Teruko and Eric H. Nyberg (1995) ‘Controlled English for Knowledge Based MT: Experience with the KANT System’, in Proceedings of the 6th International Conference on Theoretical and Methodological Issues in Machine Translation (TMI-95), Leuven, Belgium, 158−172. Mitamura, Teruko (1999) ‘Controlled Language for Multilingual Machine Translation’, in Proceedings of MT Summit VII: MT in the Great Translation Era, Singapore, 46−52. Mitamura, Teruko, Eric H. Nyberg, Kathy Baker, Peter Cramer, Jeongwoo Ko, David Svoboda, and Michael Duggan (2002) ‘The KANTOO MT System: Controlled Language Checker and Lexical Maintenance Tool’, in Stephen D. Richardson (ed.) AMTA-02: Proceedings of the 5 th Conference of the Association for Machine Translation in the Americas, Machine Translation: From Research to Real Users, 6−12 October 2002, Tiburon, CA, 244−247. Murphy, Dawn, Jane Mason, and Stuart Sklair (1998) ‘Improving Translation at the Source’, in Translating and the Computer 20, London: The Association for Information Management. Nida, Eugene A. (1969) ‘Science of Translation’, Language 45(3): 483−498. Nida, Eugene A. and Charles R. Taber ([1969] 1982) The Theory and Practice of Translation, Leiden: E.J. Brill. Nyberg, Eric H. and Teruko Mitamura (1992) ‘The KANT System: Fast, Accurate, High-quality Translation in Practical Domains’, in Proceedings of the 14th International Conference of Computational Linguistics (COLING-92), 23−28 August 1992, Nantes, France, 1069−1073. Nyberg, Eric H., Teruko Mitamura, and Jaime G. Carbonell (1997) ‘The KANT Machine System: From R&D to Initial Deployment’, in LISA Workshop on Intergrating Advanced Translation Technology, Seattle, WA. Nyberg, Eric H., Christine Kamprath, and Teruko Mitamura (1998) ‘The KANT Translation System: from R&D to Large-Scale Deployment’, LISA Newsletter 2(1): 1−7. Nyberg, Eric H. and Teruko Mitamura (2000) ‘The KANTOO Machine Translation Environment’, in John S. White (ed.) Envisioning Machine Translation in the Information Future, Berlin: Springer Verlag, 192−195. Nyberg, Eric H., Teruko Mitamura, and Willem-Olaf Huijsen (2003) ‘Controlled Language for Authoring and Translation’, in Harold L. Somers (ed.) Computers and Translation: A Translator’s Guide, Amsterdam and Philadelphia: John Benjamins, 245−281. Probst, Katharina and Lori S. Levin (2002) ‘Challenges in Automated Elicitation of a Controlled Bilingual Corpus’, in Proceedings of the 9th International Conference on Theoretical and Methodological Issues in Machine Translation (TMI-2002), Keihanna, Japan, 157−167. Rico, Celia and Enrique Torrejon (2004) ‘Controlled Translation as a New Translation Scenario − Training the Future User’, in Translating and the Computer 26, London: The Association for Information Management. S. Chan Computer-aided translation67 Rooke, Robert (1985) ‘Electronic Mail’, in Catriona Picken (ed.) Translation and Communication: Translating and the Computer 6, London: The Association for Information Management, 105−115. Roturier, Johann (2004) ‘Assessing Controlled Language Rules: Can They Improve Performance of Commercial Machine Translation Systems?’ Translating and the Computer 26, London: The Association for Information Management. Ruffino, J. Richard (1985) ‘The Impact of Controlled English on Machine Translation’, in Patricia E. Newman (ed.) American Translators Association Conference − 1985, Medford, NJ: Learned Information, Inc., 157−162. Sofer, Morry (2009) The Translator’s Handbook, Rockville, MD: Schreiber Publishing. Steiner, George ([1975] 1992) After Babel: Aspect of Language and Translation, 2nd edition, Oxford: Oxford University Press. Torrejón, Enrique (2002) ‘Controlled Translation: A New Teaching Scenario Tailor-made for the Translation Industry’, in Proceedings of the 6th Workshop of the European Association for Machine Translation: Teaching Machine Translation, Manchester, England, 107−116. van der Eijk, Pim and Jacqueline van Wees (1998) ‘Supporting Controlled Language Authoring’, in Proceedings of the 3rd Workshop of the European Association for Machine Translation: Translation Technology: Integration in the Workflow Environment, Geneva, Switzerland, 65−70. Wasson, Mark (2000) ‘Large-scale Controlled Vocabulary Indexing for Named Entities’, in Proceedings of the 6th Applied Natural Language Processing Conference, Seattle, Washington, DC, USA, 276−281. Western Standard (2011) Fluency Translation Suite 2011Fluency User Manual V2.5.04. Utah: © 2009–2011 Western Standard Translation. Available at: http://www.westernstandard.com/FluencyInstalls/ FluencyDocumentation.pdf. Wilss, Wolfram (1982) The Science of Translation: Problems and Methods, Tubingen, Germany: Gunter Narr Verlag. Windows (2012) “A History of Windows,” Windows. Wojcik, Richard H. and Heather Holmback (1996) ‘Getting a Controlled Language Off the Ground at Boeing’, in Proceedings of the 1st International Workshop on Controlled Language Applications (CLAW-96), Katholieke Universiteit Leuven, Belgium, 22−31. XTM International Ltd. (2012) XTM for CMS Explained, Bucks: XTM International Ltd. Available at: http://www.xtm-intl.com/files/content/xtm/resources/XTM%20for%20CMS%20Explained%20 2012-01.pdf. Zydron, Andrzej (2003) ‘Xml: tm – Using XML Technology to Reduce the Cost of Authoring and Translation’, in Translating and the Computer 25, London: The Association for Information Management.
TT-Assig #4 – see attached
See discussions, stats, and author profiles for this public ation at: https://www .rese archg ate.ne t/public ation/330263998 Revisiting T ranslation Quality Assu rance: A Comparative Analysis of Evaluation Principles between Student T ranslators and the Professional T rans-editor Article   in  World Journal of Education · December 2018 DOI: 10.5430/w je.v8n6p176 CIT ATIONS 2 READS 138 1 author: W an Hu Xi’an Jiaot ong-Liverpool Univ ersity 8 PUBLICA TIONS    10 CITATIONS     SEE PROFILE All c ontent f ollowing this p age w as uplo aded by Wan Hu on 20 Sept ember 2020. The user has r equested enhanc ement of the do wnloaded file. http://wje.sciedupress.com World Journal of Education Vol. 8, No. 6; 2018 Published by Sciedu Press 176 ISSN 1925-0746 E-ISSN 1925-0754 Revisiting Translation Quality Assurance: A Comparative Analysis of Evaluation Principles between Student Translators and the Professional Trans-editor Wan Hu 1,* 1School of Foreign Studies, Central University of Finance and Economics, Beijing, China *Correspondence: Wan Hu, 39 South College Road, Central University of Finance and Economics, Haidian District, Beijing 100081, China. E-mail: [email protected] This research was supported by the CUFE Research Management Office and the Teaching Research Project of Central University of Finance and Economics [grant number 2018XYJG13]. Received: December 7, 2018 Accepted: December 19, 2018 Online Published: December 21, 2018 doi:10.5430/wje.v8n6p176 URL: https://doi.org/10.5430/wje.v8n6p176 Abstract Evaluation is of paramount significance in the teaching and learning process. So is true with translation teaching and learning. This study uses in-depth interview to qualitatively examine in which ways the student translators and the professional trans-editor, two important stakeholders in the learning process, evaluate the work of translation. It then subsequently compares student translators’ and the professional trans-editor’s evaluation criteria in order to analyse the differences. This study also compares students’ pitfalls encountered during the translation process, providing students with invaluable resources to reflect on their own translation and then to improve their translation quality. An implication of this study is that the interaction among students, professional trans-editor, and university lecturers may ultimately be beneficial to translator training. Keywords: professional evaluation, peer evaluation, qualitative research, university-industry collaboration, news translation 1. Introduction With the burgeoning development of translation courses in the West and China from undergraduate to PhD levels and due to the practical nature of translation, many scholars have discussed the possibilities of balancing academic teaching and professional needs in translation programmes (Kelly, 2005; Li, 2012; Hu, 2018) from the perspective of curriculum design, teaching methods, real or simulated translation projects, and internship places. However, few research projects have been carried out on the integration of academic and professional pedagogies from the perspective of translation quality evaluation. In addition, translation students’ perceptions, an important component when looking at the translation learning process, have been examined to an even lesser degree. This study on ‘comparing student translators’ peer evaluation and professional trans-editor’s final corrections’ was conceived in this context. With an aim of investigating “who evaluates and also how to evaluate the translation work”, this paper combines the preliminary observations in the teaching of a core module about finance and economics news with in-depth interviews of student translators and the professional trans-editors. Based on the empirical data, this paper sums up the different translation evaluation principles in different contexts as well as student translators’ common pitfalls during the translation processes, and analyses the rationales behind such phenomenon. 2. Translation Quality Evaluation The evaluation of translation quality is an essential component in the translation teaching and learning process (Kelly, 2005; Colina, 2015), which is crucial to ensure whether translation learners have achieved their intended learning outcomes as well as to validate the effectiveness of the teaching syllabus. Many translation scholars have discussed quality issues from both theoretical considerations and practical applications. For example, House (1977; 1997; 2015) http://wje.sciedupress.com World Journal of Education Vol. 8, No. 6; 2018 Published by Sciedu Press 177 ISSN 1925-0746 E-ISSN 1925-0754 proposes two evaluation models of translation quality and applies these models in evaluating different types of translated texts (e.g. commercial texts; children’s literature). To be more specific, in her original model, functions of language is emphasised, analysing “linguistic-discoursal as well as the situational-cultural particularities of originals and translated texts” (ibid., 2015, p.21). The revised House model, places more emphasis on functions of texts, analysing semantical and pragmatical equivalences between the source and target texts (ibid., p.63). In addition, House (2015, p.8-15) analytically reviews different approaches to evaluating translations in accordance with different purposes, functions and preferences. These approaches are: phyco-social approaches; response-based approaches; text and discourse-oriented approaches; linguistically oriented approaches, and some specific approaches including content-oriented approach, target text oriented approach, comprehensive textual analysis, as well as others. Kelly (2005), compares functions of the traditional summative assessment methods (e.g. an unseen examination) and of the innovative formative assessment methods (e.g. a translation portfolio), from the perspective of translator training, arguing the importance of “making translation exams more realistic” (2005, p.136). In this sense, she also argues different stakeholders should be involved in the translation evaluation process. Apart from university teachers’ marking and evaluations, translation learners and experienced practitioners from the translation profession may also be included in the evaluation, giving peer feedback and industry-based feedback on students’ translation work. Mossop (2010), based on his considerable experience of translation practice, presented a clear step-by-step guideline on the revising and editing of translation work, aiming to provide translation learners and professional translators with detailed criteria and principles for improving their self-revision ability or the ability to revise the work of others. Translators, taking these principles into consideration, are made aware of how many levels of content ought to be evaluated and how many factors should be included in the translation decision-making process. This helps to assure their translation quality. With an aim of training professional translators, Orlando (2011) investigates different evaluation grids used by translation agencies, international organizations and government departments in order to help translation instructors form a more professional-based approach. However, considering the research-led teaching nature of university education, evaluating translation students’ learning processes is also of paramount importance to improving outcomes. After examining both evaluation methods from the industry and students’ academic knowledge, Orlando proposes two different evaluation grids which consist of translation product-oriented evaluation and translation process-oriented evaluation (2011, p.302-303). These two grids complement each other. Colina (2015), in her recent book Fundamentals of Translation, clarifies two different objects of evaluation, namely evaluating student translations and professional translations. The former refers to the translation work produced in the educational context, while the latter deals with translations produced for professional use. More specifically, the evaluation in the educational context is designed to help students achieve learning objectives and to serve as the instructor’s diagnostic tool. The evaluation in the professional context, however, may also assess whether the translation product meets market or industry standards, and can “contribute to professional development” (2015, p.224). Figure 1. Translation Quality Evaluation: How to Evaluate and Who to Evaluate Translation Quality Evaluation How functions of language or functions of texts summative or formative assessment detailed principles and methods Who university teacher student translators professional translators http://wje.sciedupress.com World Journal of Education Vol. 8, No. 6; 2018 Published by Sciedu Press 178 ISSN 1925-0746 E-ISSN 1925-0754 To sum up, as shown in Figure 1, the above literature on translation quality evaluation mainly focus on two dimensions: how to evaluate and who is evaluating? In answering the “how” question, evaluation approaches, frameworks and the ethos of embedding professional criteria into the evaluating process are mentioned. As regards the “who” question, university lecturers, translation students and industry experts are suggested to play their respective roles in quality assuring the translation work. Nevertheless, no research has yet tried to compare evaluation principles between student translators and professional translators, two important stakeholders in the translation learning process. In this paper, therefore, a qualitative method will be used to find out the thinking patterns between student translators and the professional trans-editor when evaluating translations, and attempts to provide illuminating examples to enrich this sub-field of Translation Studies. 3. Research Design 3.1 Research Questions This paper sets out to answer the following key questions: 1) What are the differences in revision techniques between student translators and the trans-editor? 2) What are the common translation pitfalls summarised by student translators and by the trans-editor, respectively? 3) How can these findings improve translation quality and then enrich translation teaching? 3.2 Research Methods 3.2.1 Initial Observation The initial thoughts of this study stem from the author’s teaching of a Translation of Finance and Economics News module. This module runs two semesters for year 2 students at university level. Differing from traditional teaching with a textbook, this module works collaboratively with the iNews Portal (henceforth iNews) of China Daily(Note 1). INews is a crowdsourcing trans-editing platform. The student translators need to register as translators in the iNews website, and translate or trans-edit news reports on a regular basis. To be more precise, student translators translate finance and economics news articles under the supervision of the module convenor and the professional trans-editor from China Daily. Student translators normally work in groups, and they mainly translate English news articles into Chinese, sometimes vice versa. In the group translation processes, student translators are suggested to invite their fellow students to revise the group translation work before submitting to the iNews portal for final correction. After the whole translating and evaluating processes, the module convenor compares student translators’ translation and revision with the professional trans-editor’s versions. In this model, student translators, the trans-editor and the module convenor are the three stakeholders of this module, working very closely to accomplish each task. In addition to classroom teaching, student translators can apply to the follow-up online internship offered by iNews. The internship runs 4 cohorts a year, and each cohort lasts 2 months. The student translators are afforded an enormous opportunity to practise their trans-editing skills under the supervision of China Daily’s in-house trans-editor teams. Based on the large amount of translated texts, it has been found that student translators are easily affected by the source text, and their revisions are largely at word and sentence levels. They are less experienced in revising translations from the angle of readership and of pragmatic effect. In comparison, when observing the trans-editor’s tracks and changes, the revised parts mainly bring translations in line with target readers’ expectations and evaluations of a text. Therefore, this paper aims to explore student translators and the trans-editor’s thought processes when evaluating the translation work of others. Further, through the comparison of student translators and professional trans-editors, both university lecturers and translation learners may be made aware of in which ways they can improve translation quality to better meet professional standards. With this in mind, in-depth interviews were carried out accordingly. 3.2.2 In-depth Interview According to Yin (2014, p.113), interviews are an essential source of data, and well-informed interviews can provide researchers with insights into human affairs or actions. A particular benefit of interviews is that personal experience and opinions towards a specific subject can be directly accessed (Silverman, 2013). Further, as Jiang et al. (2010, p.158) points out, interviews offer free description to capture in-depth viewpoints. However, one possible risk of interviews might be too narrative in style which may affect the validity of the research results (Saldanha & O’Brien, 2013, p.169). In this case, using structured/semi-structured interviews is an effective technique. Considering the research purposes in this study, in-depth interviews with open-ended questions was deemed an appropriate method, http://wje.sciedupress.com World Journal of Education Vol. 8, No. 6; 2018 Published by Sciedu Press 179 ISSN 1925-0746 E-ISSN 1925-0754 focusing on the “elicitation of perceptions, beliefs and motives” (Böser, 2016, p.236). Open-ended questions may leave enough space for student translators and the trans-editor to describe their individual experiences in evaluating the work of others which may in turn give the researcher a landscape of students’ common pitfalls occurring during the translation process so as to reflect on teaching activities from a pedagogical perspective (Li, 2013, p.11). On the other hand, structured interviews guide both the interviewer and the interviewees logically avoiding going off topic or losing focus. 3.3 Research Participants Nine student translators and the senior trans-editor from our industry partner were selected as interviewees to participate in this study. These interviewees’ names have been replaced by letters (interviewee A to interviewee J) to ensure anonymity. These student translators worked both as the iNews interns to evaluate and revise texts translated by other students across China and as the decision makers in their own and their peers’ group exercises. The senior trans-editor has participated in the whole teaching process of Translation of Finance and Economics News module and has plenty of experience in trans-editing English news. The profiles of these nine student translators and the trans-editor are presented in tables 1 and 2, which details their time engaged in the evaluation process and the number of texts they worked on as revisers. Table 1. Profiles of Student Translators Interviewee Gender Translation learning yearsInternship period Number of texts Length per text A B C D E F G H I F F M F F F F F F 2 years 2 years 2 years 2 years 2 years 2 years 2 years 2 years 2 years 2 months 2 months 4 months 2 months 2 months 2 months 2 months 2 months 2 months 20 20 50 23 30 20 22 20 14 1000 words 1000 words 1000 words 1000 words 1000 words 1000 words 1000 words 1000 words 1000 words Table 2. The Profile of the Trans-editor Interviewee Gender Years of revision Number of texts Length per text J M 15 years Numerous 1000 words As shown in tables 1 and 2, the maximum number of translation texts revised by student translators is 50, whilst the minimum number is 14. On average, each student translator revised at least 24 news articles translated by their peers. Their experience of evaluation accumulated continuously. The senior trans-editor has considerable experience of evaluation and has quality assured the final versions of numerous translation work. Their points of view are taken into consideration in this study. 3.4 Interview Questions The interview questions were divided into two parts targeting different groups of participants. One deals with student translators, and the other covers the trans-editor’s points of view. With regard to student translators, interview questions were asked predominantly from four perspectives. The first part contains student translators’ evaluation criteria. The second and third parts targeted mainly student translators’ observations of translation pitfalls, and their reflections on the differences between their own revisions and the editor’s final corrections. The final part deals with translation learning, aiming to figure out to what extent these evaluating experiences can improve student translators’ translation quality and further improve their translation competences. The trans-editor is interviewed mainly from the perspective of evaluation criteria as well as common pitfalls in students’ translations. http://wje.sciedupress.com World Journal of Education Vol. 8, No. 6; 2018 Published by Sciedu Press 180 ISSN 1925-0746 E-ISSN 1925-0754 4. Comparing Evaluation Criteria between Student Translators and the Professional Trans-editor 4.1 Student Translators’ Criteria for Evaluation It was noted in the conversations with student translators that their evaluation principles include the following aspects: (1) good understanding of the source text (ST), including words and sentences; (2) the terminology should be translated accurately; (3) the clumsy expressions used in the Chinese target text (TT) should be reduced; (4) logical relations in information in the target text should be dealt with carefully; (5) the translation style should be consistent with the original text; (6) register is important; (7) subject area knowledge needs to be rendered appropriately; and (8) meaning in the context should be taken into consideration. More specifically, most of the interviewees pay equal attention to the understanding of the source text and the re-expression of the target text when evaluating the work of others. In terms of the source text, student translators stress the importance of excellent mastery of subject matter and the accurate transfer of the source text information. Of these, 60% of the student translators consider the complete comprehension of the whole source text, while the rest focus more on a command of vocabulary, terminology or sentences meanings. The student translators offered more detailed criteria on the target text, namely the translation itself. After synthesising their descriptions, it was noted that: (1) accurate translation of terminology, (2) the logical relations between phrases, sentences, and paragraphs, and (3) reducing clumsy renderings were the most frequently mentioned aspects. Take terminology translation for example, interviewee A argues that “ whether the financial and economic terms are carefully dealt with when translating finance and economics news is an important criterion”. This was echoed by several other student translators. In news translation, they also stated that proper names and cultural idiosyncrasies should also be translated accurately. In addition, Interviewee D explained “collocation patterns should be in relation to the target language systems.” Then, there is the trap of over-translation, as mentioned by Interviewee H, “several student translators use Chinese idioms in their translation in order to make their language expressions more elegant. However, the selected Chinese idioms might be different from the meanings in the English source text. In this case, the evaluator need to pay special attention to this misinterpreting and make changes accordingly”. According to the student interviewees, phrases, sentences and paragraphs should be logically linked when rendered in the target text. This is also the consensus when they are evaluating their peers’ translation. Specifically, interviewee B assesses “whether each sentence coheres with other parts of the text in a reasonable way”. This perception resonates with Interviewee G, arguing that “the translations should connect together words and expressions to make the sentences coherent and to make sense to the target text readers”. Interviewee F proposes more thoughts, explaining that: ……I would consider the inner logical relations within a sentence. For example, I would read the translated sentence to check out whether it is smooth. If not, I would make changes accordingly. Moreover, I would consider the inter logical relations between sentences. Translation problems such as repetitions and incoherent content need to be corrected by student revisers. Their corresponding strategies towards logical issues include using cohesive devices and adopting the translation method of ‘transposition’ to undertake the structural change (Vinay & Darbelnet, 1958). This is particularly obvious from the English into Chinese translation. In this way, the organisation of the texts translated and assessed by student translators “are connected to each other by virtue of lexical and grammatical dependencies” (Baker, 2011, p.231), which lies in the words that both writer and reader see. Another important criterion adopted by student translators is the quality of translations. As far as the English into Chinese translation is concerned, most of them evaluate the Chinese renderings in order to avoid redundant information. For example, interviewee B described that she “condenses the length of the Chinese sentences and reduces repetitive expressions”. Interviewee D holds a similar viewpoint, evaluating “whether the words, phrases and sentences are redundant or not, paying particular attention to the news headlines; the news headlines should be translated in a concise style”. 4.2 The Trans-editor’s Criteria for Evaluation The trans-editor (interviewee J) summarises six major revising criteria in the process of evaluating and revising student translators’ work. They are: (1) the translation should offer a complete rendering of the words, expressions and ideas of the source text; (2) the translation should be entirely appropriate to the style of a newspaper, and the terminologies in certain subject areas should be rendered accurately and professionally; (3) the translation should have logical links between paragraphs; (4) the style and manner of the writing should be localised within target culture conventions; (5) the translation should read like an original piece written in the target language; (6) the http://wje.sciedupress.com World Journal of Education Vol. 8, No. 6; 2018 Published by Sciedu Press 181 ISSN 1925-0746 E-ISSN 1925-0754 translation should maintain optimal relevance with the target text readers when necessary. 4.3 Comparison of Evaluation Criteria between Student Translators and the Trans-editor The above evaluation philosophy was also reflected in the actual revising procedures of the student translators and the trans-editor. After synthesising both interview statements and revised sample texts, the major differences between student translators and the editor are summarised in table 3. Table 3. Comparison of Revision Principles of Student Translators and the Trans-editor Parameter Student translators Trans-editor Relation to ST The form of target language Effect  Loyal to the ST  Pay attention to details, such as technical terms  Consider logical relations between sentences, more at the textual level  Be aware of the redundant information  Translation awareness Focusing on the whole translating process  Respect for the form of the ST, but re-context the ST into TT context  Pay attention to key information  Consider both coherence and cohesion, more at the pragmatic level  Simpler and clearer language  Professional awareness  More conventional within news writing or trans-editing A stark contrast between student translators and the trans-editor is that student translators evaluate the “accuracy of the reproduction of the significance of the ST”, while the trans-editor assesses the “accuracy of communication of the ST message in the TT” (Munday, 2016. p.72). This can be exemplified through the translation of news headlines. Due to the inverted pyramid structure of English news, the most important information is presented in the first paragraphs (Bielsa & Bassnett, 2011, p.98), summarising the stance and the entire content. In this sense, the news headline “serves as the summary of the summary” (Cheng, 2011, p.219), using concise languages to interpret the most attractive information to news readers. Although this is a consensus in both English news headlines and Chinese headlines, they vary to a great extent in verb characteristics, sentence structure, the use of questions, and the depth and width of information (Biber & Conrad, 2009). As Li (2017, p.12) explains, English news headlines focus more on one aspect of the story, which is called “accentuation”. Chinese news headlines, however, tend to make a clean sweep of all the key information, which is called “totalism”. In translating English news headlines into Chinese, student translators prefer to stick closely to the English pattern, and revise more language expressions than content. The trans-editor, however, has a markedly different method, revising the headlines from the perspective of the whole text and conveying the intent of the source text. For example, ST headline: Amazon raises minimum wage for US and UK employees TT by student translators: 亚马逊上调英美员工最低工资 [Amazon raises US and UK employees’ minimum wage] TT by the student reviser: 亚马逊英美员工:最低工资上涨 [Amazon US and UK employees: minimum wage increasing] TT by the trans-editor: 亚马逊在舆论压力之下上调英美员工最低工资 [Amazon, under the pressure of public opinion, raises minimum wage for US and UK employees] Source: The Guardian http://wje.sciedupress.com World Journal of Education Vol. 8, No. 6; 2018 Published by Sciedu Press 182 ISSN 1925-0746 E-ISSN 1925-0754 In the original translation, student translators rendered the English headline into Chinese with full respect to the ST information, only transporting the order of the phrases without altering the meaning. The selected student translator, who is responsible for revising this piece of work, put “Amazon US and UK employees” as the subject, using a colon to explain what happened to these employees – that their minimum wage was increased. The student reviser only revised the Chinese expression, but kept the same meaning as the first version. The trans-editor, however, altered the ST headline, adding in the reasons for raising minimum wages for US and UK employees. Such added information is a generalization from the news report as a whole. The trans-editor’s version, in some sense, is more conventional with the Chinese news reporting system. As discussed previously, student translators do stress the importance of logical relations when organising the target texts. However, their habit of focusing on micro-level features of a text might prevent them from observing macro-level errors (Mossop, 2010, p.182). They evaluate to a great extent at the textual level (cohesion), namely “the network of relations which organise and create a text” (Baker, 2011, p.230), while paying less attention to the entire significance of a text (Colina, 2015, p.115). Moreover, in contrast to academic prose, in which abundant linking adverbials are employed to develop arguments and to establish cohesion (Biber & Conrad, 2009, p.121), newspaper articles contain fewer conjunctions and the text cohesion can be attributed to various sources (e.g. organisation, other documents, eye witnesses, or experts in certain fields) (ibid, 2009, p. 122). In other words, the cohesion and coherence of newspaper articles is in many cases established implicitly rather than explicitly. This also puts a demand on readers’ knowledge in perceiving the connection and also the translators’ ability in reproducing the writer’s intention in the target text. With regard to the professional trans-editor, textual interference is not a major issue. He is more concerned with interpreting the underlying semantic relations of a text. In this context, the trans-editor’s evaluation is more at the level of coherence, which is a far more pragmatic approach. According to Baker (2011, p.233), whether a text coheres or not (makes sense or not) depends on whether the reader can relate what he already knows with the knowledge presented in the text. In other words, the readers’ specialty, educational background, previous experience of both linguistic phenomenon and extra-linguistic knowledge do “influence the coherence of a text in some degrees” (ibid., p.234). From the interviews as well as the sample texts, it can be seen that the professional trans-editor has taken target readers’ cultural and intellectual background into account, and used coherence serving as a guideline to ensure accuracy in the message and logic in sense (Mossop, 2010). Thirdly, the form of the target language is more concise in the trans-editor’s revision. Highlighting the key points while removing secondary information is a method frequently used by the trans-editor. For instance, “Ali pay” and “WeChat pay”(Note 2), the top mobile payment services in China, might be mentioned in some English news articles regarding Chinese consumers’ shopping habits or Chinese digital business. These two terms might be unfamiliar concepts to readers outside China. Therefore, English reporters opt to add an explanation to these terms. But for Chinese readers, both “Ali pay” and “WeChat pay” are the most frequently used payment methods in their daily life. In the Chinese translation, the trans-editor chooses to delete such extra explanations to avoid repetition. However, student translators, when they encounter similar cases, are often afraid of deleting them. Finally, student translators may be less experienced in anticipating target readers’ sociocultural knowledge (Colina, 2015), expectations, or subject area knowledge due to a lack of work experience and real world experience. This may affect their language choices in both translation and revision. For example, there are many words and expressions having multiple meanings in different contexts. In this sense, a word or phrase might be translated correctly but not appropriately in the specific subject area. The following example regarding a Chinese into English sentence translation may better illustrate this idea. ST: ……2018年第一季度的学生留存率达到78.9%…… TT by student translators: …… Student retention rate reached 78.9% in the first quarter of 2018. TT by the student reviser: …… Student retention rate reached 78.9% in the first quarter of 2018. TT by the trans-editor: …… Student continued their enrolment at a rate of 78.9% in the first quarter of 2018. Source: Tencent News http://wje.sciedupress.com World Journal of Education Vol. 8, No. 6; 2018 Published by Sciedu Press 183 ISSN 1925-0746 E-ISSN 1925-0754 In theory, “retention rate” is the English equivalent to the Chinese phrase “留存率”, meaning the customer retention performance of a business organisation. This translation is in line with readers who have background in the education sector. However, this news article was originally a report published before a Chinese language training organization went public on the New York Stock Exchange. The amount of students who continued choosing the language courses offered by this company is an important criterion for stakeholders to consider. In this case, the trans-editor has changed the “retention rate” into “student continued their enrolment at a rate of 78.9%…”, which is more explicit and conventional in this text. The above comparisons and analysis highlight that student translators and the trans-editor have adopted different approaches to translation quality assessment due to their roles and responsibilities. As for the translation of the ST by student translators, priority is given to the assurance of transferability into the target text (Koller, 1979; House, 2015), preferring linguistically-oriented approach to evaluate the translation (ibid., 2015) and reserving the flavour of the source text (Newmark,1981). Influenced by this concept, student translators spent numerous hours assessing lexical and grammatical mismatches and then revising individual words, phrases, grammatical uses or other linguistic units. In other words, their evaluation is the pursuit of perfection in the translation itself. As a result, some revised work can still be recognised as a ‘translation’ immediately instead of an original piece of writing. The trans-editor, on behalf of his news agency, asserted his identity and articulated his power of discourse (Cheng, 2011, p.229) in the evaluation process. His approach is more response-based (House, 2015, p.10-11), stressing the importance of translation purpose, the function of the text, and readership. In this case, he has utilized his considerable trans-editing skills, and his revisions are generally more concise in content, more powerful in communication and more professional in tone. As a result, the revised translations, in many cases, can be directly used as news reports from a foreign source. 4.4 Comparison of Translation Pitfalls Summarised by Student Translators and the Trans-editor The study of translation pitfalls is “a very useful task to correct both typical and atypical deviations in the learning situation” (Cabanas, 2012, p.1439), and the discussion of student translators’ problems is a common practice in the classroom (Gile, 2004). In this study, all the participants have been asked to generalize student translators’ translation problems in order to contribute further insights into translation teaching from the perspective of English-Chinese translation, a linguistically distant language pair. Table 4 presents the details. Table 4. Translation Pitfalls Summarised by Student Translators and the Trans-editor Translation Pitfalls Student Translators The Trans-editor At decoding level (source text) At encoding level (target text) Formatting Issues  Guessing at the meaning of words instead of considering the context or consulting a dictionary, especially with polysemous words  Misunderstanding of English idioms, metaphoric rhetoric and marked collocations  Were unable to comprehend the meaning of a whole sentence or paragraph and therefore resulting in awkward renditions  Difficult to change the structure of the ST in the TT and therefore resorting to translationese  Complicated expressions in the TT, especially with headlines  Organisation was not wholly logical  Inappropriate translation of proper names  Occasional errors in punctuation  Some words and sentences were misunderstood  Inadequate understanding of the information within the context  Not proficient at researching documents and subject area knowledge  Inappropriate interpretation of certain terms and expressions  Difficult to deal with Complex sentences or “Russian doll” sentences which contain additional layers of information  Some renditions were not arranged coherently, or did not follow a logical sequence  Expressions were not concise http://wje.sciedupress.com World Journal of Education Vol. 8, No. 6; 2018 Published by Sciedu Press 184 ISSN 1925-0746 E-ISSN 1925-0754 As viewed in table 4, the trans-editor and student translators share a few things in common. They summarise translation problems from both the comprehension of the source language and the output in the target language. They also believe that student translators may have misunderstandings of certain technical terms, especially when these terms have implied meanings. Some student translators may have a partial understanding of sentences and paragraphs but do not place them within the correct context. Further, both the trans-editor and student translators have observed that students are less likely to deal with complex sentences during the translating process. It is difficult for them to slice away long sentences into shorter ones, and then link those shorter sentences to produce semantically and syntactically sound translations. This is actually the biggest part for editorial changes. From a discourse perspective, student translators and the trans-editor are concerned with the overall organisation of students’ translations. It can be also seen from table 4 that student translators place greater emphasis on translation pitfalls at and above word level, while the trans-editor pays more attention to the misunderstanding of translation content as well as misinterpretation of sentence structures. In light of the above analysis, revising peer translators’ work is an invaluable experience for student translators to develop the ability to evaluate and to justify decisions as well as comment on them (Kelly, 2005, p.143), which is an essential skill in the translation profession. Summarising common translation problems made by their fellow students benefits student translators by making them reflect on their own translating processes and to think out of the box. Receiving feedback from professional trans-editors outside of university also provides learners with opportunities to take professional criteria into account, and above all, to improve their translation quality. 5. Conclusion and Implications This research set out to provide readers with qualitative and descriptive data on translation evaluation criteria. In view of the comparison of the student translators and the professional trans-editor, the translation evaluation criteria of these two groups differs in the following aspects: (1) different purpose and stance for evaluation. The professional trans-editor evaluates the work of translation from the perspective of target readers’ needs and reading habits as well as newspaper writing conventions, while student translators place greater emphasis on a loyal renderings of the original news article; (2) different types of evaluation. The trans-editor tends to highlight key elements in a news story while “feeling free to clarify obscurities” (Munday, 2016, p.45). Student translators concentrate on details in the translating process such as lexical mismatches and syntactic mismatches; (3) different effects after evaluation. The trans-editor is more sensitive to the different linguistic conventions between the English and Chinese languages, and the revised translations read more like original news reports without altering the original meaning. Student translators, on the other hand, are more easily affected by the writing style and manner of the source text, and their revised works of translation can still be recognised as translations. The different preferences and cognitions in translation evaluation between the professional trans-editor and student translators may have wider implications for translator trainers and learners. Through the comparison of student translators’ peer evaluations and the trans-editor’s final versions, the university lecturer can be made aware of students’ progress and difficulties in learning, as well as the gap between their current level and market needs. From the perspective of translation learners, the generalization of common translation pitfalls made by their peers helps them to think about relative coping strategies to improve their translation quality. In addition, excellent translations encountered during the their evaluation process may also serve as invaluable learning resources. Furthermore, using this method, students are afforded an overall understanding of how the media industry works. With the prerequisite that most translation programmes aim to cultivate high-calibre professional translators, “it is rational to acknowledge what the professional standards are” (Hu, 2018, p.9) and then to decide what and how to embed these standards into classroom teaching. Finally, the knowledge and expertise of one university can also be transferred to other institutions. This research has examined the different evaluation principles between student translators and the professional trans-editor in a Chinese context. Similar research can be designed and conducted in academic contexts in other regions of the world. English-Chinese translation practices can also be transferred to other language pairs. This can further enrich the field of translator training and translation practice. Acknowledgments The author wants to thank the two anonymous reviewers for their insightful comments, and thank Peter McSweeney for his constructive discussions about this study. http://wje.sciedupress.com World Journal of Education Vol. 8, No. 6; 2018 Published by Sciedu Press 185 ISSN 1925-0746 E-ISSN 1925-0754 References Baker, M. (2011). In Other Words: A Coursebook on Translation. London and New York: Routledge.   https://doi.org/10.4324/9780203832929 Biber, D., & Conrad, S. (2009). Register, Genre and Style. Cambridge: Cambridge University Press.   https://doi.org/10.1017/CBO9780511814358 Bielsa, E. & Bassnett, S. (2011). Translation in Global News. Shanghai: Shanghai Foreign Language Education Press. Böser, U. (2016). Interviews and focus groups. In C. V. Angelelli & B. J. Baer (Eds.), Researching Translation and Interpreting (pp. 236-246). London and New York: Routledge. Cabanas, J. M. O. (2012). Learning English as a Foreign Language: Translation and Error Analysis. US-China Foreign Languages, 10(8), 1431-1443. Cheng, W. (2011). Innovative subjectivity of transeditors in intercultural communication–a case study of the translated news of the 2008 Olympic Games. Language and Intercultural Communication, 11(3), 215-231. https://doi.org/10.1080/14708477.2010.549567 Colina, S. (2015). Fundamentals of Translation. Cambridge: Cambridge University Press.   https://doi.org/10.1017/CBO9781139548854 Gile, G. (2004). Integrated Problem and Decision Reporting as a Translator Training Tool. The Journal of Specialised Translation, 2, 2-20. House, J. (1977). A Model for Translation Quality Assessment. Tübingen: Narr. House, J. (1997). Translation Quality Assessment: A Model Revisited. Tübingen: Narr. House, J. (2015). Translation Quality Assessment: Past and Present. London and New York: Routledge. Hu, W. (2018). Education, Translation and Global Market Pressures: Curriculum Design in China and the UK. Singapore: Springer Nature. https://doi.org/10.1007/978-981-10-8207-8 Jiang, X., Napoli, R. D., Borg, M., Maunder, R., Fry, H., & Walsh, E. (2010). Becoming and being an academic: the perspectives of Chinese staff in two research-intensive UK universities. Studies in Higher Education, 35(2), 155-170. https://doi.org/10.1080/03075070902995213 Kelly, D. (2005). A Handbook for Translator Trainers. Manchester: St. Jerome Publishing. Koller, W. (1979). Einführung in die Übersetzungswissenschaft. Heidelberg-Wiesbaden: Quelle und Meyer. Li, D. (2012). Curriculum Design, Needs Assessment and Translation Pedagogy. Berlin: LAP LAMBERT Academic Publishing. Li, D. (2013). Teaching Business Translation: A Task-based Approach. The Interpreter and Translator Trainer, 7(1), 1-26. https://doi.org/10.1080/13556509.2013.10798841 Li, D. (2017). News Translation: Principles and Methods (2nd ed.). Hong Kong: Hong Kong University Press. Mossop, B. (2010). Revising and Editing for Translators. Manchester: St. Jerome. Munday, J. (2016). Introducing Translation Studies: Theories and Applications (4th ed.). London and New York: Routledge.  https://doi.org/10.4324/9781315691862 Newmark, P. (1981). Approaches to Translation. Oxford and New York: Pergamon. Orlando, M. (2011). Evaluation of Translations in the Training of Professional Translators. The Interpreter and Translator Trainer, 5(2), 293-308. https://doi.org/10.1080/13556509.2011.10798822 Saldanha, G., & O’Brien, S. (2013). Research Methodologies in Translation Studies. London and New York: Routledge.(Note 3) Silverman, D. (2013). Doing Qualitative Research (4th ed.). London: SAGE Publications Ltd. Vinay, J. P., & Darbelnet, J. (1958). Stylistique comparée du français et de l’anglais: Méthode de traduction. Paris: Didier. Yin, R.K. (2014). Case Study Research Design and Methods (5th ed.). CA: Sage. http://wje.sciedupress.com World Journal of Education Vol. 8, No. 6; 2018 Published by Sciedu Press 186 ISSN 1925-0746 E-ISSN 1925-0754 Notes Note 1. China Daily is a national English-language newspaper in China, including both print media and digital media. A voice of China on the global stage. Note 2. Ali pay is supported by Alibaba, a leading platform for global wholesale trade (https://www.alibaba.com/?spm=5386.1223793.a273ac.42.atANxm) Wechat Pay is supported by Tencent, an internet-based technology and cultural enterprise (https://www.tencent.com/en-us/index.html) .They are the Chinese equivalents of Apple Pay. View publication statsView publication stats
TT-Assig #4 – see attached
6/26/22, 8:21 PM Google Translate adds 24 new languages, including its first indigenous languages of the Americas | T echCrunch https://techcrunch.com/2022/05/11/google-translate-adds-24-new-languages-including-its-first-indigenous-languages-of-the-americas/ 1/9 Goo g le T ra n sla te a d ds 2 4 n ew l a n g uag es, i n clu d in g i t s fir s t i n d ig en o us l a n g uag es o f t h e A m eric a s Sara h P ere z @ sa ra h in ta m pa / 1 :1 7 P M ED T •Ma y 1 1, 2 022 Image Credits: Google In a d dit io n to im pro vin g G oogle A ssis ta nt’s a bilit y to c o m munic a te w it h u se rs in a m ore n atu ra l w ay, G oogle to day a nnounce d im pro ve m ents to it s G oogle T ra nsla te s e rv ic e . T he c o m pany s a id it ’s a ddin g 2 4 n ew la nguages — in clu din g it s fir s t in dig enous la ngu ages o f th e A m eric a s w it h th e a ddit io ns o f Q uech ua, G uara ni a nd A ym ara . O th er a ddit io ns in clu de th e fo llo w in g: In to ta l, th e 2 4 n ew la nguages a re s p oke n b y m ore th an 3 00 m illio n p eople w orld w id e, G oogle s a id . T u rn e q uit y i n to l iq uid it y L o gin S earc h TC S essio ns: R o bo tic s S ta rtu p s Te ch C ru n ch + A ud io N ew sle tte rs S ta rtu p B attle fie ld A dve rtis e E ve nts M ore J oin T e ch C ru n ch + G et a c ce ss t o e xp ert a d vic e o n f u n d ra is in g , g ro w th , a n d m an ag em en t f o r y o ur s ta rtu p . 6/26/22, 8:21 PM Google Translate adds 24 new languages, including its first indigenous languages of the Americas | T echCrunch https://techcrunch.com/2022/05/11/google-translate-adds-24-new-languages-including-its-first-indigenous-languages-of-the-americas/ 2/9 “T his r a nges fr o m s m alle r la nguages, lik e M iz o s p oke n b y p eople in th e n o rth east o f In dia — b y a bout 8 00,0 00 p eople — u p to v e ry la rg e w orld la nguages lik e L in ga la s p oke n b y a ro und 4 5 m illio n p eop le a cro ss C entr a l A fr ic a ,” s a id Is a ac C asw ell, a G oogle T ra nsla te R ese arc h S cie ntis t. H e a d ded th at in a ddit io n to th e in dig enous la nguages o f th e A m eric a s, G oogle T ra nsla te w ill s u pport a d ia le ct o f E nglis h fo r th e fir s t tim e w it h K rio fr o m S ie rra L eone. T he c o m pany s a id it s e le cte d th is n e w est b atc h o f la nguages to s u pport b y lo okin g fo r la ngu ages w it h v e ry la rg e b ut u nders e rv e d p opula tio ns — w hic h w ere fr e q uently in th e A fr ic a n c o ntin ent a nd In dia n su bco ntin ent. It a ls o w ante d to a ddre ss in dig enous la nguages, w hic h a re o fte n o ve rlo oke d b y te ch nolo gy. T e ll u s a b out y o urs e lf Y o u c o uld w in a n ew A pp le i P ad m in i! T A KE T H E S U RV EY Spo nso re d b y L in q to L o gin S earc h TC S essio ns: R o bo tic s S ta rtu p s Te ch C ru n ch + A ud io N ew sle tte rs S ta rtu p B attle fie ld A dve rtis e E ve nts M ore J oin T e ch C ru n ch + G et a c ce ss t o e xp ert a d vic e o n f u n d ra is in g , g ro w th , a n d m an ag em en t f o r y o ur s ta rtu p . 6/26/22, 8:21 PM Google Translate adds 24 new languages, including its first indigenous languages of the Americas | T echCrunch https://techcrunch.com/2022/05/11/google-translate-adds-24-new-languages-including-its-first-indigenous-languages-of-the-americas/ 3/9 More T e ch C ru nch G oogle ’s a bilit y to a dd n ew la nguage s h as im pro ve d th anks to te ch nolo gic a l a dva nce s ta kin g p la ce o ve r th e p ast fe w y e ars , C asw ell s a id . “U p u ntil a c o uple o f y e ars a go, it s im ply w as n ot te ch nolo gic a lly p ossib le to a dd la nguages lik e th ese , w hic h a re w hat w e c a ll a lo w r e so urc e — m eanin g th at th ere a re n ot v e ry m any te xt r e so urc e s o ut th ere fo r th em ,” h e e xp la in ed. B ut a n ew te ch nolo gy ca lle d Z ero -S hot M ach in e T ra nsla tio n h as m ade it e asie r. “ A t a h ig h le ve l, th e w ay y o u c a n im agin e it w ork in g is y o u h ave a s in gle g ig antic n eura l A I m odel, a nd it ’s tr a in ed o n 1 00 d if f e re nt la nguages w it h tr a nsla tio n. Y ou c a n th in k o f it a s a p oly g lo t th at kn ow s lo ts o f la nguages. B ut th en a d dit io nally , it g ets to s e e te xt in 1 ,0 00 m ore la nguages th at is n ’t tr a nsla te d. Y ou c a n im agin e if y o u’r e s o m e b ig p oly g lo t, a nd th en y o u ju st s ta rt r e adin g n ove ls in a noth er la nguage, y o u c a n s ta rt to p ie ce to geth er w hat it c o uld m ean b ase d o n y o ur k n ow le dge o f la nguage in g enera l, ” h e s a id . T he e xp ansio n b rin gs th e to ta l n um ber o f la nguages s u pporte d b y th e s e rv ic e to 1 33. B ut G oogle s a id th e s e rv ic e s till h as a lo ng w ay t o g o, a s th ere a re s till s o m e 7 ,0 00 u nsu pporte d la nguages g lo bally th at T ra nsla te d oesn ’t a ddre ss. T he n ew la nguages w ill b e liv e to day o n G oogle T ra nsla te , b ut w on’t r e ach a ll u se rs w orld w id e fo r a c o uple o f d ays, G oogle n ote d . W elc o m e t o T e ch C ru nch c o m men ts ! P le ase k e ep c o n ve rs a tio n s c o u rte o u s a n d o n -t o p ic . S e e o u r c o m munit y g u id elin es f o r m ore in fo rm atio n . C on ve rs a tio n Lo g in S ig n u p B e t h e rs t t o c o m men t… L o gin S earc h TC S essio ns: R o bo tic s S ta rtu p s Te ch C ru n ch + A ud io N ew sle tte rs S ta rtu p B attle fie ld A dve rtis e E ve nts M ore J oin T e ch C ru n ch + G et a c ce ss t o e xp ert a d vic e o n f u n d ra is in g , g ro w th , a n d m an ag em en t f o r y o ur s ta rtu p .