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Fit-For-Market Translator and Interpreter Training in a Digital Age

Edited by Rita Besznyák Márta Fischer Csilla Szabó

With an introduction by Juan José Arevalillo

Series in Language and Linguistics

Copyright © 2020 by the Authors.

Reviewed by Marcel Thelen (Maastricht School of and Interpreting, Zuyd of Applied Sciences )

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Series in Language and Linguistics

Library of Congress Control Number: 2019956381

ISBN: 978-1-62273-862-5

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Table of contents

Editors’ Preface and Acknowledgements vii

Introduction xi

Part I. Market-oriented practices and collaborative e-learning in translator and interpreter training 1

Chapter 1 Fit-for-market specialised translator and interpreter training: a Hungarian example 3 Csilla Szabó Budapest University of and Economics, Centre for Interpreter and Translator Training

Chapter 2 Roadmap for e-learning implementation in higher 25 Zita Krajcso University of Vienna, Centre for

Chapter 3 Creating, testing and using e-learning modules in a specialised translation programme: A study on the eTransFair open educational resources 43 Barbara Heinisch University of Vienna, Centre for Translation Studies

Part II. Competence development: towards an extended translator profile 63

Chapter 4 Developing terminology competence, with special focus on recognising terms 65 Márta Fischer Budapest University of Technology and Economics, Centre for Modern Languages

Chapter 5 Acquisition of post- competence: training proposal and its outcomes 85 Olena Blagodarna Universidad Autónoma de Barcelona

Chapter 6 Beyond CAT: The question of digital literacy in translator training 103 Melinda Dabis Pázmány Péter Catholic University, Budapest

Part III. Neural and post-editing in translator training 117

Chapter 7 Rage against the machine – will post-editing assignments outnumber in the future? 119 Réka Eszenyi 1 – Brigitta Dóczi 2 1ELTE Budapest, Department of Translation and Interpreting 2ELTE Budapest, Department of English Applied Linguistics

Chapter 8 Humans, machines, and texts: The implications of the rise of neural machine translation for the educators of future translators 135 Tímea Kovács Károli Gáspár University of the Reformed Church, Institute of English Studies, Department of English Linguistics

Chapter 9 MT output and post-editing effort: Insights from a comparative analysis of SMT and NMT output and implications for training 151 Maria Stasimioti – Vilelmini Sosoni Ionian University, Department of Foreign Languages, Translation and Interpreting

Part IV. New – new methods in the work and training of interpreters and translators 177

Chapter 10 Human language technologies and digitalisation in a multilingual interpreting setting 179 Ram ūnas Česonis European Commission

Chapter 11 Increasing source text difficulty in interpreter training projects by analysing lexical pitfalls 195 Rita Besznyák Budapest University of Technology and Economics, Centre for Interpreter and Translator Training

Chapter 12 in translator training in a digital age 213 Viktor Zachar Eötvös Loránd University, Budapest, Department of Translation and Interpreting

List of abbreviations and acronyms 225

List of illustrations 229

Index 231

Editors’ Preface and Acknowledgements

We are living at an exciting time. It is a time when the language is being shaped by sweeping technological advances, with the increasing sophistication of machine translation and the rapid development of artificial intelligence. It is also a time when these achievements are gradually finding their way into the everyday practice of translators and interpreters, which means that these novel ideas and technologies will also need to be included in their training. More than ever, translator and interpreter training institutions have to respond to newly emerging market needs, prepare their graduates for varying job profiles and a transition to their working lives – which makes knowledge sharing and collaboration between the market and the academia indispensable. With these developments in mind, the Centre for Modern Languages at the Budapest University for Technology and Economics (BME) envisaged a transnational cooperation scheme with a focus on specialised translators, which set out to the gap between educational outcomes and the actual demands of the translation market. This vision came to fruition under the aegis of eTransFair, an Erasmus+ Strategic Partnership project that was launched and coordinated by our Centre, working in close cooperation with the Centre for Translation Studies of the University of Vienna (Univie) and Hermes Traducciones, a key market player in the translation industry, based in Madrid. Between September 2016 and August 2019 the project produced a number of intellectual outputs to ‘achieve inclusive, fit-for-market and transferable specialised translator training’: a competence card for specialised translators, a transferable training scheme, e-learning materials and virtual collaboration spaces, along with a set of manuals designed to make the results adaptable and sustainable in the long run. The major achievements of the eTransFair project were disseminated to a wider audience at an international conference entitled ‘Fit-for-market translator and interpreter training in a digital age’, held in September 2018 in Budapest, at the Centre for Modern Languages (BME). The conference intended to provide an international forum for discussing topical issues in the field of translator and interpreter training with a focus on current market requirements and digital trends. It also invited potential partners for the European Centre for Online Specialised Translation (e-COST, in brief) set up within the framework of eTransFair. Based on a selection of contributions, this constitutes the proceedings of the conference, covering a wide range of practical and theoretical issues. viii Editors’ Preface and Acknowledgements

Four subjects of high relevance are discussed in 12 chapters: 1. collaborative partnership in the field of fit-for-market practices with a focus on e-learning materials, 2. competence development in translator and interpreter training, 3. the implications of neural machine translation and the increasing significance of post-editing practices and 4. the role of new technologies and new methods in the work and training of interpreters and translators. The book is a written record of the key achievements of the eTransFair project and the thoughts and ideas put forward at the conference. The first part of the book offers valuable tips on how translator training institutions can keep abreast of the insatiable demands of the translation industry and how they can best prepare their graduates to respond to the fresh challenges they will inevitably face. Based on a Hungarian example, the current practice of the Centre for Interpreter and Translator Training (CITT) at BME’s Centre for Modern Languages, the first paper (by Csilla Szabó ) draws on the findings of the eTransFair project to offer advice on what curricular changes training institutions may choose to adopt, and what (extra-)curricular options they may offer as part of their programmes (e.g., mentoring schemes, Trainees Meet Professional series, extended traineeships). This part of the book also features an Austrian example (by Zita Krajcso ), detailing the efforts made by the Centre for Translation Studies of the University of Vienna in implementing e-learning. The paper addresses concerns that are associated with the introduction of e-learning and illustrates ideas for the of support measures – offering a general roadmap for e-learning implementation in higher education institutions. In a similar vein, the third paper (by Barbara Heinisch ) focuses on the e-learning materials (e-modules) developed by the eTransFair project. Two pilot studies were conducted to assess the quality of these modules, providing a basis for further development. Their findings highlight the importance of iterative design, the constant negotiation of learning outcomes and the need for an introduction to each activity along with clear task descriptions. The second part of the book addresses the challenges of competence development, focusing on terminology, post-editing and digital literacy. The first paper (by Márta Fischer ) demonstrates how a broad, translation- oriented approach was applied when compiling the ‘terminology and info- ’ e-module of the eTransfair project, considering terminology as a separate competence in its own right. Two activities are presented along with worksheets and sample texts, focusing on one specific sub-competence, the recognition of terms. The second paper (by Olena Blagodarna ) offers a training proposal that seeks to develop trainees’ post-editing competence, taking a learner-centered and motivation-based approach. Assessed by a pre- /post-test model, the proposal proved to be an efficient and language- Editors’ Preface and Acknowledgements ix independent tool to improve trainees’ post-editing competence. Based on students’ responses to a questionnaire, the third paper (by Melinda Dabis ) explores students’ IT skills, computer and smartphone using habits and perceived knowledge of software. Digital trends and the implications of machine translation are discussed in the third part of the book. The first paper (by Réka Eszenyi and Brigitta Dóczi ) presents the results of a multi-phase research project, in which the translation skills of human translator trainees were compared with the Machine Translator of the European Commission (eTranslation). The findings suggest that MT is likely to take over the task of human translators in a wide range of translation scenarios which would justify the inclusion of pre- and post-editing skills in translator training courses. Similar conclusions are drawn in the next chapter (by Tímea Kovács ) on the basis of a study in which target language texts produced by phrase-based (PBMT) and neural language machine (NMT) translation tools were compared to human-translated target language texts by means of a text-based micro-analysis. The paper also concludes that NMT produces more appropriate translations in terms of faithfulness and well-formedness. The last chapter, a case study (by Maria Stasimioti and Vilelmini Sosoni ), explores the differences in levels of cognitive effort while post-editing the outputs of neural machine translation as compared to those of statistical machine translation, by means of eye- tracking and keystroke data. Its findings also underline the need to take into consideration the quality of MT systems and the errors found in the raw MT output in post-editing training. The emergence of new technologies and their impact and usability in the work and training of interpreters and translators is the main focus of the last part of our book. The first paper (by Ram ūnas Česonis ) reports on the effort of the European Commission’s Directorate-General for Interpretation (DG SCIC), and in particular, the Task-Force on New Technologies and Digital Transformation to explore the benefits of existing technological processes and projects in interpreting. An exciting initiative in this field is the interpreter's workbench project, a portal that might serve as a useful resource and toolkit for document analyses and glossary . Remaining on the topic of interpreting, but this time from a trainer’s perspective, the next chapter (by Rita Besznyák ) intends to offer an effective tool for finding and creating speeches for use in interpreter training and for gradually increasing the level of source text difficulty through the analysis of lexical pitfalls. The paper presents the results of a project aimed at raising awareness of potential lexical difficulties by using online resources and analysing parallel texts. Linguistically-founded methodological considerations form the basis of the last chapter (by Viktor Zachar ), as well. He highlights the benefits of using x Editors’ Preface and Acknowledgements journalistic texts in translator training and claims that various types of journalistic translation have the potential to develop the terminological and revision competence of translation students, thus contributing to a relatively new research area in Translation Studies. We are thankful to our project partners, Univie and Hermes Traducciones, who contributed to the successful completion of the eTransfair project. We are particularly indebted to Juan José Arevalillo , managing director of Hermes Traducciones, whose presence and commitment, as well as extensive experience as a lecturer in a market-oriented subject in various training programmes across Spain, ensured the inclusion of market aspects at every stage of our collaboration. We would also like to express our gratitude to our colleagues at BME for their collective efforts throughout the project. We also thank the participants of the conference and the authors of this volume for their contributions and submitted papers. Above all, our deep gratitude is owed to the reviewer of this book, Marcel Thelen , whose diligence, devoted professional support and invaluable comments ensured the quality of the papers, which will make this publication, we hope, a reference book for further discussions. We trust that this volume will create, similarly to our project, fresh momentum for researchers, academics, professionals and trainees to be engaged in a constructive dialogue about the exciting challenges and novel solutions emerging in our profession.

The Editors

Introduction

When I started my professional career in the field of translation back in 1985, nothing made me think that a professional translators’ profile could experience an unbridled career, hand in hand with technology. At that time, translators were divided into the two major groups of either literary or technical translators, the latter being those who did not do literary translations, so the sub- categorisation of specialisations was enormous. At that time, technology was not foremost in the lives of translators, whose image to others was that of someone being isolated and surrounded by and dictionaries, with an aura of intellectuality that still persists – but always working in the shadow, unnoticed even, by the majority of mortals. The technology at their disposal consisted of a typewriter – an electric one if you were lucky enough – and little else. However, after the emergence of the personal computer and the first office systems, as simple as they might be, many of us thought that technology could offer us little more as we had already moved from a purely analogue environment to a (proto-)digital environment that allowed us greater productivity, going from the traditional machine to the first word processors; and from there, we saw in a very short period of time a visual user computing system that allowed us to create and layout documents with functions undreamed just a few years earlier. Since the advent of the personal computer, there was yet another turning point: translation memories, with which many rented their clothes while prophesying the end of the days for translators. A situation very similar to the one that occurred with the appearance of machine translation and its subsequent developments to the present day, without forgetting either specialised tools to control the quality of translations, their terminological coherence, their precision with figures and other endless features in the mind of the translator who knows about macros and other programming boundaries. Nowadays, no one doubts that these multiple technological developments help improve professional translators’ productivity and specialise their tasks. Technology has taken us by the hand with its advances, impregnating the whole of society and all sectors, including translation. This sector is now called language services or the language industry since linguists, translators, philologists and people with similar profiles not only translate but also perform very complementary technical tasks for other sectors or disciplines, although mainly with the invisibility of their work. As if that were not enough, this evolution took place in a time span of approximately ten years! xii Introduction

Unfortunately, all this evolutionary process of translation was suffered in their own flesh and blood by veteran translators, without formal training and relying on their own initiative. Companies and looked to different sides without understanding that an educational and professional synergy was necessary so that these future translators and linguists could enter the labour market in a more relaxed way and thus reduce their learning curve. Many of us who have suffered from this training gap have considered that we should offer our help to the university world so that its programmes reflect the professional reality of the translator that is so much changing that it requires the continuous updating of technology. All the more as in addition to theoretical training that a translator needs, it is also necessary for future practitioners to become familiar with the technological atlas that awaits them in the real world. These synergy efforts have almost always come initially from the individual interest of professionals or lecturers, rather than from an institutional approach. Fortunately, this scenario has changed, and many efforts have been made by universities and translation companies trying to bring positions closer together and exchange ideas in order to adapt the syllabus to professional reality, even with the support of various EU-funded projects. One of them, eTransFair, in which I have been lucky enough to participate, sought precisely that, namely, to build those and to continuously cross them back and forth, in order to produce training modules that can help better understand the reality of the market that graduates can expect to meet. In this project, the workflow between university and company has been exemplary, and hence its practical and theoretical results. We all hope that its intellectual outputs will be of help in the training of future professionals from the perspective of a universe of applied technology that never ceases to grow and surprise us.

Juan José Arevalillo

managing director Hermes Traducciones, Madrid

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List of abbreviations and acronyms

AI artificial intelligence API application programming interface AUSIT Australian Institute of Interpreters and Translators BLEU bilingual evaluation understudy BME Budapest University for Technology and Economics CAT computer-assisted translation CC BY Creative Commons Attribution Licence CHEPS Center for Higher Education Policy Studies CITT Centre for Interpreter and Translator Training (BME) CIUTI International Standing Conference of University Institutes of Translators and Interpreters CONS consecutive CTL Centre for Teaching and Learning (UniVie) DG SCIC Directorate General for Interpreting at the European Commission DORIS Data Oriented Services DQF Dynamic Quality Framework ECDL European Computer Driving Licence E-COST European Centre for Specialised Translators (eTransFair) ECTS European Transfer and Accumulation System EHEA European Higher Education Area ELIA European Language Industry Association ELITR European Live Translator project ELTE Eötvös Loránd University, Budapest EMT European Master’s in Translation EP European Parliament EQF European Qualification Framework EUATC European Union of Associations of Translation Companies EUROSAI European Organisation of Supreme Audit Institutions 226 List of abbreviations and acronyms

EuroVoc EU's multilingual and multidisciplinary thesaurus FET future and emerging technologies FIT International Federation of Translators FOMO Fear of Missing Out GALA Globalization and Localization Association GDPR General Data Protection Regulation HGW home gateway HLT human language technologies HMT human-made translation IAMLADP International Annual Meeting on Language Arrangements, Documentation and Publications IATE Interactive Terminology for Europe ICT Information and Communication Technologies IEC International Electrotechnical Commission ISO International for Standardizatio ITCB Information Technology and Cybersecurity Board L1 first language LINDWeb Language Industry Web Platform LMS learning management system LSP languages for specific purposes MeLLANGE Multilingual eLearning in Language META NET Multilingual Europe Technology Alliance Network MFTE Association of Hungarian Translators and Interpreters MI machine interpreting MT machine translation MTI Hungarian News Agency MTPE machine translation post-editing NMT neural machine translation OER open educational resources OFFI Hungarian Office for Translation and Attestation OPI over-the-phone interpreting OS operating system List of abbreviations and acronyms 227

PACTE Process of Acquisition of Translation Competence and Evaluation PAS publicly available specification PAT Pool of Assessment Techniques (eTransFair) PBMT phrase-based machine translation PE post-editing PEEMPIP Panhellenic Association of Professional Translation Graduates of the Ionian University PEM Panhellenic Association of Translators PROFORD Association of Hungarian Professional Translation Providers RBMT Rule-Based Machine Translation SAP Systems, Applications and Products SD standard deviation SDI Speech Difficulty Index SIDP simultaneous interpreting delivery platform SMT Statistical Machine Translation ST source text ST source language ST source text SVO Subject – Verb – Object SZOFT Association of Hungarian Freelance Translators and Interpreters TAUS Translation Automation User Society TL target language TransCert Trans-European Voluntary Certification for Translators TS testing sessions TT target text UAB Universitat Autònoma de Barcelona UAD user activity data Univie University of Vienna UNSW University of New South Wales VRI video remote interpreting 228 List of abbreviations and acronyms

WER word error rate WIPO World Intellectual Property Organisation WPH word per hour ZFHD Zeitschrift für Hochschuldidaktik ZID Computer Centre (Zentraler Informatikdienst, UniVie) ZTW Centre for Translation Studies (Zentrum für Translationswissenschaft, UniVie)

List of illustrations

List of figures

Figure 3.1. Localisation module: "I rate the tested activity as..." 57 Figure 3.2. Localisation module: "I learn a lot in this module" 57 Figure 3.3. Prior experience of students 58 Figure 9.1. Participants’ years of experience 155 Figure 9.2. Tasks involved in participants’ work 156 Figure 9.3. Participants’ years of PE experience 156 Figure 9.4. Participants’ training in PE 156 Figure 9.5. Participants’ interest in a PE course 157 Figure 9.6. Participants’ view on PE training 157 Figure 9.7. Screenshot of a PE task in Translog-II, where the focus of gaze data is represented by a circle and its mapping by a rectangle over the respective portion of the text being fixated 158 Figure 9.8. Screenshot of a PE task in Translog-II 161 Figure 9.9. Subset of the DQF-MQM typology for MT analysis (Lommel and Melby, 2018) 164 Figure 9.10. The core error categories proposed by the MQM guidelines 165 Figure 9.11. Error typology based on the subset of the DQF/MQM typology and the MQM guidelines 165

List of tables

Table 4.1. Information-mining and terminology competence in eTransFair 73 Table 4.2. Units of the terminology e-module 74 Table 4.3. Key to Activity 2/ Unit 1 of the terminology e-module 76 Table 5.1. WPH rates: core measurements and Wilcoxon test results 93 Table 5.2. Under-edited segments: core measurements and Wilcoxon test results 94 Table 5.3. Fit-for-purpose segments: core measurements and Wilcoxon test results 94 Table 5.4. Over-edited segments: core measurements and Wilcoxon test results 95 230 List of illustrations

Table 5.5. Associative correlations: WPH rates and post-edited segments (TS 1) 96 Table 5.6. Associative correlations: post-edited segments (TS 1) 96 Table 5.7. Associative correlations: WHP rates and post-edited segments (TS 2) 97 Table 5.8. Associative correlations: post-edited segments (TS 2) 97 Table 7.1. The SL text and the eTranslation output 127 Table 8.1. Summary of inappropriate uses in the NMT and PBMT English translations of the Hungarian sentence 143 Table 8.2. Summary of inappropriate uses in the PBMT English translations of the Hungarian sentence 145 Table 8.3. Quantitative summary of inappropriate uses in NMT and PBMT English translations of a Hungarian sentence 146 Table 9.1. Participants’ gender, age , education level and degree type 155 Table 9.2. Lexile® scores for the source texts used in the study 162 Table 9.3. Automatic evaluation scores per text 163 Table 9.4. Mean for adequacy and fluency per MT system based on human evaluation 166 Table 9.5. Error types per MT system 166 Table 9.6. Temporal effort per MT system: Mean and standard deviation values of the task duration 168 Table 9.7. Technical effort per MT system: mean and standard deviation values for the total number of keystrokes, insertions and deletions 169 Table 9.8. Cognitive effort per MT system: Mean and standard deviation values of the fixation count, the fixation duration and the gaze time 170 Table 11.1. Overall structure of the interpreting project ‘Smartphone Sucht’ 208 Table 11.2. Occurrence of lexical pitfalls in the corpus of the Smartphone-Sucht Project 210

Index

A E adaptation, 217 e-COST, 19, 60, 72 agglutinative, 141 ECTS, 26 artificial intelligence, 3, 6, 114, 122, Effort Model, 200 135, 181, 191, 194 e-learning, 25, 31, 44, 45, 50, 51, augmented interpreter, 179, 182 56, 59 autonomous learning, 87, 89 e-learning material, 43, 45 e-learning strategy, 26, 28 B e-modules, 50, 52, 54, 72, 73 error annotation, 164, 170 Bilingual Evaluation Understudy, error typology, 165 162 eTransFair, 3, 6, 7, 8, 10, 20, 21, 28, blended learning, 26, 28, 29 43, 44, 45, 47, 48, 59, 70, 72, 78, 79 C eTranslation, 121, 123, 127 European Higher Education Area, CAT tools, 104, 107, 109, 110, 112, 86, 88, 98 130 European Master’s in Translation, cognitive effort, 167, 169, 170, 171, 6, 8, 46, 66, 123 174, 175, 204 evaluation, 126 competence development, 39, 46, eye-tracking, 151, 152, 153, 159, 72 160, 170, 173 competence model, 8, 65, 66, 77 competence-based training, 87 F coping strategies, 197, 206, 210 formal learning, 36 D H DG SCIC, 181, 183 digital literacy, 108 human translation, 124, 142 digital skills , 105 distance interpreting, 182, 190 I DORIS, 187, 188 Dynamic Quality Framework, 89 ICT, 5, 39, 46, 48, 107, 188 ICT-based education, 72 inflectional-isolating, 141 232 Index informal learning, 36 N input variables, 198, 199, 200, 207 interpreter training, 182, 184, 195, neural machine translation, 4, 114, 196, 198, 203 122, 123, 129, 136, 137, 138, 140, interpreting exam, 207 152, 172, 192 interpreting project, 197, 208 news translation, 215 IT skills, 103 newspaper articles, 217 IT training , 105 NMT system, 122 non-professional translators, 217 J O journalism, 214 journalistic translation, 213 open educational resources, 44, 45, 48, 59 L P language service providers, 215 lexical pitfalls, 197, 203, 205, 208, PE competence, 90, 91, 92, 95, 98 210 PE courses, 171 localisation, 56, 58 PE training, 152, 153, 171 low-resource language pairs, 147 phrase-based machine translation, 138, 139 M Pool of Assessment Techniques, 11 post-editing, 86, 87, 88, 91, 98, machine interpreting, 185, 191, 120, 126, 130, 151 192 post-editing competence, 85, 88 machine translation (MT), 3, 85, programmed learning, 88 120, 151 machine translation post-editing, R 86, 90 market demands, 5 remote interpreting, 190, 191 marketability, 5, 6 media didactics, 29 S mental lexicon, 125 mentoring scheme , 11, 12, 14, 19 simultaneous interpreting monitoring, 125, 126 delivery platforms, 190, 191 MT output, 88, 127, 151, 162 smartphone, 105, 106, 108, 109, multilingualism, 180 110, 209 social media, 6, 105, 108 soft skills, 5, 6, 10, 14, 18 source text difficulty, 198 specialised translation, 10, 44, 46, 50, 53 Index 233

Statistical Machine Translation, transfer operations, 220 99, 173 translation didactics, 44, 51, 54 translation techniques, 220 T translator competence , 5, 6, 89 Translog-II, 158 Technical Effort , 168 temporal effort, 167 V term extraction, 185 term recognition, 67, 77, 183 virtualisation, 190 terminology, 65, 69 terminology competence, 65, 66, W 72, 73, 74 text production, 221 Wikipedia, 221 text types, 219 Word Error Rate, 162 traineeship , 16, 19