{"id":2078,"date":"2019-11-21T11:12:37","date_gmt":"2019-11-21T11:12:37","guid":{"rendered":"https:\/\/www.asling.org\/tc41\/?page_id=2078"},"modified":"2019-12-26T20:56:59","modified_gmt":"2019-12-26T20:56:59","slug":"presentations","status":"publish","type":"page","link":"https:\/\/www.asling.org\/tc41\/?page_id=2078","title":{"rendered":"Download Presentations"},"content":{"rendered":"

Proceedings also available<\/span><\/a>X<\/span><\/a><\/p>\n\n\n\n\n
Presentations became available here shortly after they were presented
\nThe slides of all presentations are available for download; see below
\n<\/span><\/em><\/td>\n<\/tr>\n
21 November<\/span><\/strong><\/td>\ngo to 22 November<\/span><\/strong><\/a><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n\n\n\n\n\n\n\n\n
Day 1<\/strong><\/span><\/td>\n 1st<\/sup> morning Session <\/strong>– Chair: Jo\u00e3o Esteves-Ferreira<\/strong><\/span><\/td>\n<\/tr>\n
\u00a0<\/td>\nSponsors’ Thought Leadership Talks<\/span><\/strong><\/td>\n<\/tr>\n
9.10<\/strong><\/span><\/td>\nGold Sponsor: STAR<\/strong><\/span> Judith Klein<\/span><\/em>
\nAre we ready for the (M)Translation Future?<\/strong><\/a> (21 slides)<\/span><\/td>\n<\/tr>\n
9.25<\/strong><\/span><\/td>\nSilver Sponsor: XTM International<\/strong><\/span> Elizabeth Butters, Business Development Manager, and Andrzej Zydron, CTO<\/span><\/em>
\nThe Translation Management System for Global Enterprises<\/strong><\/a> (10 slides)<\/span><\/td>\n<\/tr>\n
9.25<\/strong><\/span><\/td>\nSilver Sponsor: SDL<\/strong><\/span> Vicenta Ten Soriano, Regional Sales Director <\/span><\/em>
\nThe Intelligent Translation Era<\/strong><\/a> (26 slides)<\/span><\/td>\n<\/tr>\n
9.45<\/strong><\/span><\/td>\nKeynote speaker<\/strong><\/span>
<\/span>Jean Senellart (Systran)<\/div>
[text-bloThe Intelligent Translation Eracks id=”818″ slug=”jean-senellart-bio”]<\/div><\/div><\/span> The Neural Revolution in the Translation Industry \u2013 3-Year Retrospective and Future Directions<\/strong><\/a> (41 slides)<\/span><\/td>\n<\/tr>\n
10.45<\/strong><\/span><\/td>\n
<\/span>Andrzej Zydro\u0144 (XTM Int.)<\/div>

\"\"Andrzej Zydro\u0144<\/strong> (MBCS CITP) is one of the leading IT experts on Localization and related Open Standards. Zydro\u0144 sits\/has sat on the following Open Standard Technical Committees:<\/p>\n

    \n
  1. LISA OSCAR GMX<\/li>\n
  2. LISA OSCAR xml:tm<\/li>\n
  3. LISA OSCAR TBX<\/li>\n
  4. W3C ITS<\/li>\n
  5. OASIS XLIFF<\/li>\n
  6. OASIS Translation Web Services<\/li>\n
  7. OASIS DITA Translation<\/li>\n
  8. OASIS OAXAL<\/li>\n
  9. ETSI LIS<\/li>\n
  10. DITA Localization<\/li>\n
  11. Interoperability Now!<\/li>\n
  12. Linport<\/li>\n<\/ol>\n

    Zydro\u0144 has been responsible for the architecture of the essential word and character count GMX-V (Global Information Management Metrics eXchange) standard, as well as the revolutionary xml:tm standard which will change the way in which we view and use translation memory. Zydro\u0144 is also head of the OASIS OAXAL (Open Architecture for XML Authoring and Localization technical committee.<\/p>\n

    Zydro\u0144 has worked in IT since 1976 and has been responsible for major successful projects at Xerox, SDL, Oxford University Press, Ford of Europe, DocZone and Lingo24.<\/p>\n<\/div><\/div><\/span>

    <\/span>De-demonizing AI (Abstract)<\/div>

    AI has garnered much hype over the past few years. Andrzej Zydro\u0144 provides a realistic definition of AI: what is intelligence; how can it be defined; what is the mathematical basis for intelligence, as well as detailing the theoretical limitations of AI and what is actually achievable. The presentation will detail the actual practical potential of AI as well as its limitations and pitfalls when human beings interact with AI systems.<\/span><\/p>\n<\/div><\/div><\/span> De-demonizing AI<\/strong><\/a> (30 slides)<\/span><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n\n\n\n\n\n
    Day 1<\/strong><\/span><\/td>\n 2nd<\/sup> morning Session <\/strong>– Chair: Ruslan Mitkov<\/strong><\/span><\/td>\n<\/tr>\n
    11.45<\/strong><\/span><\/td>\n
    <\/span>Ale\u0161 Tamchyna (Memsource)<\/div>

    \"\"Software Engineer, Head of the AI Team at Memsource. Ales joined Memsource in spring 2017 as the first member of the artificial intelligence (AI) team. He works on integrating smart solutions based on machine learning and AI in the Memsource platform. Before joining the company, he was an academic researcher. His primary research topic was machine translation.<\/span><\/p>\n<\/div><\/div><\/span>

    <\/span>Applying AI to NT and MT (Abstract)<\/div>

    In the translation industry, the disruptive effect of AI is not yet apparent. Machine learning\/AI has traditionally been associated with one feature: machine translation (MT). It is true that with the recent advancements in neural MT, the output quality is inching closer to human translation. However, neural MT still makes serious mistakes and its quality can be upset by more complex sentences. More importantly, professional translation has different standards than simply passing for human translation; translations might require a specific style, consistent terminology, coherence across sentences and paragraphs, etc. But a translator\u2019s goal is to convey the original meaning as closely as possible. They have to carefully navigate ambiguity and craft wording that best reflects the emphasis in the original text, ensuring that there can be no confusion about the meaning. Consider the severity of mistakes within legal texts or medical records; in marketing, a good translation can be the difference between a successful campaign and an international embarrassment. It\u2019s clear that MT is not going to replace human translation anytime soon, if ever.<\/span><\/p>\n<\/div><\/div><\/span>Applying AI to NT and MT<\/strong><\/a> (24 slides)<\/span><\/td>\n<\/tr>\n

    12.15<\/strong><\/span><\/td>\n
    <\/span>Margita \u0160o\u0161tari\u0107, Nata\u0161a Pavlovi\u0107 and Filip Boltu\u017ei\u0107 (Univ. of Zagreb)<\/div>

    \"\"<\/p>\n

    Nata\u0161a Pavlovi\u0107<\/strong> is an associate professor in the Faculty of Humanities and Social Sciences, University of Zagreb, where she teaches translation theory and practice. She holds a PhD in Translation and Intercultural Studies from the University Rovira i Virgili in Tarragona, Spain. Her research interests include translation process research, translator education, and translation technology, in particular machine translation and post-editing.<\/span><\/p>\n

    \"\"<\/p>\n

    Margita \u0160o\u0161tari\u0107<\/strong> is a recent graduate from the Faculty of Humanities and Social Sciences, University of Zagreb and currently works at Omega Software, a software development company in Zagreb. Her research interests span from the more theoretical approaches to language, such as cognitive linguistics, to the direct applications of language processing, such as machine translation.<\/span><\/p>\n

    \"\"Filip Boltu\u017ei\u0107<\/strong> is a PhD researcher\u00a0 in the Text Analysis and Knowledge Engineering Lab (TakeLab) in the Faculty of Electrical Engineering and Computing, University of Zagreb. His main research interests are natural language processing and argument mining. Prior to joining TakeLab, he worked at Zagreba\u010dka banka Unicreditgroup as a data analyst and Amazon Data Services Ireland as a software development engineer.<\/p>\n<\/div><\/div><\/span>

    <\/span>Domain Adaptation for Machine Translation Involving a Low-Resource Language: Google AutoML vs. Form-Scratch NMT System (Abstract)<\/div>

    Despite the advances in machine translation achieved with neural models, adaptation of such systems for specialist domains remains a challenge. The problem is particularly acute when it comes to low-resource languages. <\/span><\/p>\n

    Additionally, the computational resources and expertise needed to train neural models present barriers to entry for smaller translation companies and freelancers. In such cases, paid but affordable customization services such as Google Cloud AutoML might present a viable solution. In this study, domain adaptation using Google Cloud AutoML Translation is compared to a more traditional scenario, where several neural machine translation systems are trained from scratch using OpenNMT, an open-source toolkit for machine translation. <\/span><\/p>\n

    The from-scratch systems are trained using a larger out-of-domain English-Croatian dataset and a smaller in-domain English-Croatian dataset comprised of medical texts. The same in-domain data are used to customize Google Cloud AutoML Translation. The performance of the systems is compared using automatic and human evaluation methods. The resources, skills and time necessary to set up the examined systems are also discussed.<\/span><\/p>\n<\/div><\/div><\/span>Domain-adapted MT<\/strong><\/a> (28 slides)<\/span><\/td>\n<\/tr>\n

    12.45<\/strong><\/span><\/td>\n
    <\/span>Ayla Rigouts Terryn, Lieve Macken, Els Lefever, Robert Vander Stichele, Koen Vanneste and Joost Buysschaert (Uni. of Ghent)<\/div>

    \"\"Ayla Rigouts Terryn<\/strong> is a PhD scholar at the LT3 language and translation technology team at Ghent University. She has a master in Translation from Antwerp University and researched translation revision competence at her alma mater. In 2015, she started at Ghent University to pursue her interests in natural language processing on the SCATE (Smart Computer-Aided Translation Environment) project. She currently holds a PhD scholarship from the Research Foundation – Flanders to study monolingual and multilingual automatic terminology extraction from comparable corpora. Her other research interests include medical translation and the difference between laypeople and specialists for terminology and translation tasks.<\/span><\/p>\n

    \"\"Lieve Macken<\/strong> is Assistant Professor at the Department of Translation, Interpreting and Communication at Ghent University (Belgium). She has strong expertise in multilingual natural language processing. Her main research focus is translation technology and more specifically the comparison of different methods of translation (human vs. post-editing, human vs. computer-aided translation), translation quality assessment, and quality estimation for machine translation. She is the operational head of the language technology section of the department, where she also teaches Translation Technology, Machine Translation and Localisation.<\/span><\/p>\n

    \"\"Els Lefever<\/strong> is an assistant professor at the LT3 language and translation technology team at Ghent University. She has a master in Linguistics and Literature (Romance languages) and holds a PhD in computer science from Ghent University on ParaSense: Parallel Corpora for Word Sense Disambiguation (2012). She started her career as a computational linguist at the R&D-department of Lernout & Hauspie Speech products. She has a strong expertise in machine learning of natural language and multilingual natural language processing, with a special interest for computational semantics, cross-lingual word sense disambiguation and multilingual terminology extraction. Currently, she supervises PhD research on the automatic extraction of topics, stance and argumentation from social media text, extracting terminology from comparable text, resolving ambiguous terms in cross-disciplinary collaboration and the automatic linking of medical lay and professional terminology to enhance comprehension of medical texts by patients. She is an executive board member of SIGLEX, the Special Interest Group on the Lexicon of the Association for Computational Linguistics and co-director of the Ghent Centre for Digital Humanities. She teaches Terminology and Translation Technology, Language Technology and Digital Humanities courses.<\/span><\/p>\n

    \"\"Joost Buysschaert<\/strong> is emeritus professor of the Department of Translation, Interpreting and Communication of Ghent University (Belgium), where he used to teach translation technology and medical translation (among other courses). He has remained active within the Department\u2019s Terminology Centre (www.cvt.ugent.be) and continues to publish on terminology, translation tools and translation training. Among the terminology projects that he is involved in, is the MeSH Termbase Project on English and Dutch medical terminology (http:\/\/www.cvt.ugent.be\/mesh.htm). He advises the company ivs iscientia on the use of translation tools.<\/span><\/p>\n<\/div><\/div><\/span>

    <\/span>Pilot Study on Medical Translations in Lay Language: Post-Editing by Language Specialists, Domain Specialists or Both? (Abstract)<\/div>

    Despite the rich history of research into medical translation, there is a notable lack of empirical studies on the best workflow for this task, especially in a modern translation setting involving post-editing of machine translation. This pilot study was conducted in preparation for a large translation project of medical guidelines for laypeople from Dutch into French. It is meant to shed light on how medical post-editing is best handled. How do medical specialists (doctors) versus language specialists (translators) perform on this task? How can their respective strengths lead to the highest quality translation? To gain more insight into these questions, errors in the machine translation output of medical guidelines were annotated and labelled. <\/span><\/p>\n

    Based on these annotations, the product of doctors’ and translators’ post-editing could be analysed and classified into necessary changes (mistakes that were correctly solved), underrevisions (mistakes that were not corrected during post-editing), overrevisions (new errors introduced during post-editing) and hyperrevisions (preferential changes made by the post-editor). <\/span><\/p>\n

    The results of this small-scale research illustrate the complexity of the task and reveal some surprising findings (e.g., doctors sometimes struggle with domain-specific terminology, and translators appear to be less efficient because they introduce many hyperrevisions).<\/span><\/p>\n<\/div><\/div><\/span>Pilot Study on Medical Translations in Lay Language<\/strong><\/a> (32 slides)<\/span><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n\n\n\n\n\n
    Day 1<\/strong><\/span><\/td>\n 1st<\/sup> afternoon Session <\/strong>– Chair: Juliet M. Macan<\/strong><\/span><\/td>\n<\/tr>\n
    14.00<\/strong><\/span><\/td>\n
    <\/span>Rodolfo Maslias (European Parliament)<\/div>

    \"\"Rodolfo Maslias<\/strong> is born in Thessaloniki (GR) in 1957. He studied languages, German, French and Spanish and speaks also Italian, Dutch and English. He post-graduated in Giessen (D) in the German Classical Theatre (schiller) and Romanistik. He entered as translator in the European Parliament in 1981 and works since then in the Directorate General for Translation. With secondments or as external activity he worked for culture as Director of the International Programme of \u201cThessaloniki, European Capital of Culture 1997\u201d, Head of Cabinet of the Minister of Culture and cultural advisor to the Mayor of Athens and was elected for ten years Coordinator of the Network of European Capitals of Culture. In 2008 he was asked to create the Terminology Coordination Unit of the European Parliament. He teaches Terminology in Master Courses in several Universities and is member of the Bureau of TermNet and of several scientific committees in the field of Terminology. He has published books on culture, terminology, as well as essays and poems.<\/span><\/p>\n<\/div><\/div>

    <\/span>New Audiences for EU Terminology (Abstract)<\/div>

    A short PowerPoint presentation followed by live surfing in the public websites termcoord.eu<\/a>,\u00a0YourTerm.org<\/a> and in the (password protected) EU interinstitutional terminology portal EurTerm, focusing on the terminology management in the European Parliament, the cooperation between EU Institutions at central (EurTerm) and at language levels (wikis), the collaboration with the interpreters in the EU for terminology, the interoperability of the new version of IATE, the efforts of TermCoord for a new terminologist profile in the recruiting procedures in the EU Institutions, the connection of the EU and other terminology resources (like the EP\u2019s GlossaryLinks) with the NMT and the post editing and quality control software, terminology projects with Universities with and for IATE, Master courses on terminology at the Universities of Luxembourg (36 hours), Savoie-Mont Blanc (21 hours) and Orientale Napoli (11 hours)\u00a0 and occasionally in many Universities (Vigo, Germersheim, ISIT Paris a.o.) and the new approach of \u201cplain terminology\u201d projects, adapted to communication needs and addressed to the civil society with the programme \u201cTerminology without Borders\u201d in several fields and in collaboration with specialised EU Agencies and International Organisations and with specialised departments of Universities in several European countries.<\/span><\/p>\n<\/div><\/div><\/span> New Audiences for Terminology<\/strong><\/a> (44 slides)<\/span><\/td>\n<\/tr>\n

    14.30<\/strong><\/span><\/td>\n
    <\/span>Denis Dechandon (EPO), Maria Recort Ruiz (ILO) and Aniko Gerencser (EPO)<\/div>

    \"\"Denis Dechandon<\/strong> has over 20 years\u2019 experience in translation and linguistics, in office automation and in different management roles. After getting acquainted with the translation work and its requirements at EU level, he fully committed himself to the definition and implementation of processes and workflows to provide structured and efficient support to linguists and to streamline the work of support teams.<\/span><\/p>\n

    Previously in his last role, Denis was responsible for leading a service dedicated to the linguistic and technical support provided to translators, revisers, editors, captioners and\u00a0 subtitlers (Computer Assisted Translation, corpus management, formatting and layouting, machine translation and terminology). Additionally, he supervised the maintenance and development of tools and linguistic resources at the Translation Centre for the Bodies of the European Union. Committed to further changes and evolutions in these fields, Denis took over the role of InterActive Terminology for Europe (IATE) Tool Manager from May 2015 to August 2017.<\/span><\/p>\n

    In his current role as Head of the Metadata sector of the Publications Office of the European Union, he is leading the activities in standardization (in particular: EuroVoc and registry of metadata) as well as intensely involved in the field of linked open data at the Publications Office of the European Union. Latest projects involve the development of synergies between several different stakeholders, such as EU institutions, agencies and bodies, international organisations and national public services.<\/span><\/p>\n

    \"\"<\/p>\n

    Maria Recort Ruiz<\/strong> is a philologist, translator and terminologist who works as Document Services Coordinator and Terminology Manager at the International Labour Organization in Geneva. She is responsible for the production and management of official documents, management of terminology work and the use of new CATT tools to improve working methods. She holds a Degree in Slavic Philology from the University of Barcelona, where she specialized in Russian and Polish Language and Literature, and Linguistics; a Master in French and Comparative Literature (19th-20th centuries) from the University of Montpellier, where she conducted research on the roman populaire at the beginning of the 20th century; and a Master in Specialized Translation from the University of Geneva. Before joining the ILO, she worked as a freelance translator and editor for international organizations and the private sector.<\/span><\/p>\n

    \"\"Anik\u00f3 Gerencs\u00e9r<\/strong> holds a Master`s Degree in Library and Information Science and a PhD in Italian Language and Literature from the University ELTE of Budapest.<\/span><\/p>\n

    Since joining the Publications Office of the European Union she has been working in the field of metadata standardisation and linked open data management. Her particular area of responsibility is the co-maintenance of the EuroVoc multilingual thesaurus and its alignment with other controlled vocabularies. She currently works on the optimisation of the thesaurus management tool Vocbench3 which involves analysing users` needs and improving collaborative features. She is in charge of providing presentations, consultancy and trainings regarding the use of VocBench3 for EU institutions. In addition she strongly contributes to an on-going project that aims to achieve interoperability between controlled vocabularies by sharing common tools and formats for the creation, use and maintenance of vocabularies and taxonomies.<\/span><\/p>\n<\/div><\/div>

    <\/span>Terminology: Towards a Systematic Integration of Semantics and Metadata (Abstract)<\/div>
    <\/div><\/div><\/span>Terminology: Towards a Systematic Integration of Semantics and Metadata<\/a><\/strong> (21 slides – as presented)<\/span>
    \n     Terminology: Towards a Systematic Integration of Semantics and Metadata<\/strong><\/a> (31 slides – extended slide set)<\/span><\/td>\n<\/tr>\n
    15.00<\/strong><\/span><\/td>\n
    <\/span>Jean-Francois Richard (Terminotix)<\/div>

    \"\"Having a background in automated productivity, Jean-Fran\u00e7ois Richard<\/strong> has worked in the field of computer aided translation tools for the last thirty years. Through different experiences of work, he acquires expertise in translation memory systems, terminology extraction tools, terminology management, machine translation, full text, bitexts and project management systems. During his career, he develops SynchroTerm, a powerful terminology extraction tool allowing the feeding of terminology databases from translated documents. In September 2006, Jean-Fran\u00e7ois Richard joined the Terminotix team where he occupied the Sales Director position. In April 2010, he acquired Terminotix and since then, he is the president of the company. Under his leadership, the company has grown at a rate of more than 20% per year.<\/span><\/p>\n

    Terminotix is a software development company that helps linguistic services and language\u00a0service providers increase performance by automating and enhancing the translation process. Terminotix helps translators, revisers, coordinators and terminologists increase their productivity by up to 50% with a complete suite of products designed and optimized for language professionals.<\/span><\/p>\n<\/div><\/div><\/span>

    <\/span>Terminology Extraction as a Tool for MT Output Assessment and Improvement (Abstract)<\/div>

    The present paper proposes the use of a Terminology Recall Index (TRI) calculated on retaining nominal groups\u2019 frequencies and stemming info only. <\/span><\/p>\n

    Though this paper proposes to demonstrate the utility of a TRI calculation between a human translated document and neural machine translated document, it also attempts to demonstrate that a broader use of the TRI calculation has many other surprising applications inside a linguistic service’s translation workflow.<\/span><\/p>\n<\/div><\/div><\/span>Terminology extraction as a tool for MT output<\/strong><\/a> (6 slides)<\/span><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n\n\n\n\n\n
    Day 1<\/strong><\/span><\/td>\n 2nd<\/sup> afternoon Session <\/strong>– Chair: Ruslan Mitkov<\/strong><\/span><\/td>\n<\/tr>\n
    16.00<\/strong><\/span><\/td>\n
    <\/span>Maria Stasimioti and Vilelmini Sosoni (Ionian University)<\/div>

    \"\"Maria Stasimioti<\/strong> is a PhD candidate in the Department of Foreign Languages, Translation and Interpreting at the Ionian University. She holds a BA in Translation Studies and an MA in Theory and Didactics of Translation from the same university. She has been working as a freelance translator and proofreader since 2010. She has taught Computer-Assisted-Translation and English for Specific Purposes (ESP) at the Ionian University. She has also participated in the EU-funded project TraMOOC (Translation of Massive Open Online Courses, https:\/\/tramooc.eu\/ ). Her research interests lie in the areas of Machine Translation (NMT, SMT), Computer-Assisted-Translation (CAT) and Post Editing (PE).<\/span><\/p>\n

    \u00a0<\/span><\/p>\n

    \"\"Dr Vilelmini Sosoni<\/strong> is Assistant Professor in the Department of Foreign Languages, Translation and Interpreting at the Ionian University in Corfu, Greece, where she teaches Legal and Economic Translation, EU texts Translation and Terminology, Translation Technology, Translation Project Management and Audiovisual Translation (AVT). In the past, she taught Specialised Translation in the UK at the University of Surrey, the University of Westminster and Roehampton University, and in Greece at the National and Kapodistrian University of Athens and the Institut Fran\u00e7ais d\u2019 Ath\u00e8nes.\u00a0 She also has extensive industrial experience having worked as translator, editor and subtitler. <\/span><\/p>\n

    She holds a BA in English Language and Linguistics from the National and Kapodistrian University of Athens, an MA in Translation and a PhD in Translation and Text Linguistics from the University of Surrey. Her research interests lie in the areas of Corpus Linguistics, Machine Translation (MT), Cognitive Studies, Translation of Institutional Texts and AVT. She is a founding member of the Research Lab \u201cLanguage and Politics\u201d of the Ionian University and a member of the \u201cCentre for Research in Translation and Transcultural Studies\u201d of Roehampton University. She has participated in several EU-funded projects, notably TraMOOC, Eurolect Observatory and Training Action for Legal Practitioners: Linguistic Skills and Translation in EU Competition Law, while she has edited several volumes and books on translation and published numerous articles in international journals and collective volumes.<\/span><\/p>\n<\/div><\/div><\/span>

    <\/span>Undergraduate Translation Students\u2019 Performance and Attitude towards Machine Translation and Post-editing: Does Training Play a Role? (Abstract)<\/div>

    In an effort to meet the demands in speed and productivity, while keeping the cost low, the translation industry has turned to Machine Translation (MT) and Post-Editing (PE). <\/span><\/p>\n

    Nowadays, it is common practice to include MT in the translation workflow by using MT output as raw translation to be further post-edited by a translator (Lommel and DePalma, 2016). Yet, translators still approach PE with caution and skepticism and question its real benefits (Koponen 2012; Gaspari et al 2014; Moorkens 2018). In addition, attitudes to MT and PE seem to affect PE effort and performance (Witczak, 2016; \u00c7etiner and \u0130\u015fisa\u011f, 2019). Under that light, this study aims to investigate the attitudes and perceptions of undergraduate translation students towards MT and PE as well as their performance before and after they receive training in MT and PE. <\/span><\/p>\n

    Questionnaires are used to capture their attitudes and perceptions, while a human evaluation of their post-edited MT output is used to assess their performance and the quality of the post-edited texts. The analysis reveals a change in the students\u2019 attitudes and perceptions; they report a more positive attitude toward MT and PE, they are more confident and faster, while they avoid over-editing.<\/span><\/p>\n<\/div><\/div><\/span>Undergraduate Translation Students\u2019 Performance and Attitude towards Machine Translation and Post-editing<\/strong><\/a> (33 slides)<\/span><\/td>\n<\/tr>\n

    16.30<\/strong><\/span><\/td>\n
    <\/span>Christopher Gledhill and Maria Zimina (Univ. Paris-Diderot)<\/div>

    \"\"Christopher Gledhill<\/strong> is Professor of English Linguistics at the Universite\u0301 Paris Diderot (Paris, France) where he is currently director of Languages for Specialists of all Disciplines (LANSAD), coordinator of Masters in Applied Foreign Languages (Mention LEA) and cocoordinator with Natalie Ku\u0308bler of a research Masters in Languages for Specific Purposes, Corpus Linguistics and Translation studies (Master LSCT). He currently teaches and conducts research in interlinguistics, specialised translation, phraseology, systemic functional grammar and text linguistics.<\/span><\/p>\n

    \"\"Maria Zimina-Poirot<\/strong> is Associate Professor in English Language Studies at Paris Diderot (Universite\u0301 Paris Diderot). She holds a PhD in Language Studies and Linguistics from Paris 3 \u2013 Sorbonne nouvelle University (2004). Her PhD thesis focused on developing new tools for textometric exploration of multilingual text corpora. Between 2005-20012 she worked as a teaching and research assistant at Paris Nord \u2013 Paris 13 University, INaLCO, INSERM, etc. and as a technical writer for Orange Business Services (Paris). Her current teaching and research activities concern textometric analysis of multilingual corpora, text typologies, controlled languages, terminology, technical writing and computer-aided translation.<\/span><\/p>\n<\/div><\/div><\/span>

    <\/span>The Impact of Machine Translation on a Masters Course in Web Translation: From Disrupted Practice to a Qualitative Translation\/Revision Workflow (Abstract)<\/div>

    The introduction of technology into translation curricula is a complex task in terms of translation competences and their acquisition. Computer tools and MT directly affect trainee translators. <\/span><\/p>\n

    This study investigates the impact of technology on students on a Master’s in Specialised Translation and Language Industries at Universit\u00e9 Paris Diderot. We present the results of a teaching project \u201cWebsite translation into English\u201d which places strong emphasis on hands-on applications of MT. <\/span><\/p>\n

    The aim of the project is to provide students with a semi-professional work experience in which they face real-life website translation problems. Students are expected to translate and revise webpages from French into English using a professional platform SystranLINKS. The first results of our study show that a more equipped translator\u2019s workstation results in assisted but also disrupted translation practice, and requires additional learning\/teaching time. Intensive practice of MT raises students\u2019 awareness of the importance of a revision workflow, and gives students a broader understanding of translation quality. <\/span><\/p>\n

    Our methodology involves the analysis of project reporting forms, which students write at the end of the course as a record of their learning experience. We examine both their explicit comments and their implicit metalanguage, in order to explore how they conceptualise MT.<\/span><\/p>\n<\/div><\/div><\/span>The Impact of MT on a Masters Course in Web Translation<\/strong><\/a> (26 Slides)<\/span><\/td>\n<\/tr>\n

    17.30<\/strong><\/span><\/td>\n
    <\/span>Marion Kaczmarek and Michael Filhol (LIMSI)<\/div>

    \"\"Marion Kaczmarek<\/strong> is a PhD student in both linguistics and computer sciences, in the quite particular field of Sign Language. Former French Sign Language Interpreter, trained at the University of Rouen (France) where she also graduated with a Language Sciences Master\u2019s degree, her studies have led her from cognitive sciences to Sign Language linguistics. Her PhD involves both Sign Language Translation and CAT software, trying to find ways of equipping the Sign Language translators with computer assistance. Her work started in 2018, joining the CNRS (the French nation scientific research center) and other associates on a more global project concerning media accessibility.<\/p>\n

    \"\"Marion Kaczmarek<\/strong> is a PhD student in both linguistics and computer sciences, in the quite particular field of Sign Language. Former French Sign Language Interpreter, trained at the University of Rouen (France) where she also graduated with a Language Sciences Master\u2019s degree, her studies have led her from cognitive sciences to Sign Language linguistics. Her PhD involves both Sign Language Translation and CAT software, trying to find ways of equipping the Sign Language translators with computer assistance. Her work started in 2018, joining the CNRS (the French nation scientific research center) and other associates on a more global project concerning media accessibility.<\/p>\n

    \"\"Michael Filhol<\/strong> is a computer scientist who has always been passionate about languages and linguistics. He naturally turned to NLP in his studies, which took place in France and Ireland after the year 2000, always learning new languages and comparing how they work as a side hobby.<\/span><\/p>\n

    He chose to focus on Sign Language for his PhD, the most exotic linguistic system he knew, and which he had been learning since high school. Largely still unknown to science and virtually absent in the NLP field, he addressed and proposed a formal description model of signs for Sign Language synthesis by 3D avatars. He defended his PhD in 2008 at Universit\u00e9 Paris Sud (Orsay, France), and continued his research career on Sign Language processing.<\/span><\/p>\n

    He stayed for a post-doctoral year at Gallaudet University (Washington, DC, USA), where all classes and services are accessible in Sign Language, and some of the most famous researchers on Sign linguistics are hosted. Back in France, he got his permanent researcher position at CNRS (the French national scientific research centre), where he kept working on the formal description and computer implementation of Sign Language.<\/span><\/p>\n

    He eventually proposed AZee, a formal approach capturing all levels of discourse and capable of driving a 3D avatar to animate Sign from a combination of semantic operations. It is now used by the world leaders in Sign synthesis, as input for their animation platforms. While always improving and extending the coverage of the AZee approach, his research interests have grown to encompass more topics like graphical writing systems for Sign Language, or automatic and assisted text-to-Sign translation.<\/span><\/p>\n<\/div><\/div>

    <\/span>Assisting Sign Language Translation: what Interface Given the Lack of Written Form and the Spatial Grammar? (Abstract)<\/div>

    Computer-assisted translation (CAT) software offers tools for the translators to ease their tasks, and gain time as well as comfort. However, despite the growing need for Sign Language content, there has been no effort to equip Sign Language translation with CAT software. The problem we address here is the specification of such software. Sign Languages are visual and iconic, with grammar and discourse organisation, but also no written form. This is problematic when it comes to CAT, for it relies on editable written structures and the fact that the concatenation of the translated segments will result in the translation of the concatenated source segments (we call it the linearity assumption).<\/span><\/p>\n

    In this paper, we explain that Sign Language cannot follow those rules. We address those differences by means of new adapted modules which would be more flexible, and by considering new tools based on professionals\u2019 feedback towards their actual practice as well as the problems they encounter during the translation process. We will detail those results along with the presentation of how we envisage a sign language concordancer, and its database.<\/span><\/p>\n<\/div><\/div><\/span>Assisting Sign Language Translation<\/strong><\/a> (13 slides)<\/span><\/a><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

     <\/p>\n\n\n\n
    22 November<\/span><\/strong><\/td>\ngo to 21 November<\/span><\/strong><\/a><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n\n\n\n\n\n\n\n\n\n
    Day 2<\/strong><\/span><\/td>\n 1st<\/sup> morning Session <\/strong>– Chair: Olaf-Michael Stefanov<\/strong><\/span><\/td>\n<\/tr>\n
    \u00a0<\/td>\nSponsors’ Thought Leadership Talks<\/span><\/strong><\/td>\n<\/tr>\n
    9:05<\/strong><\/span><\/td>\nGold Sponsor: Terminitox<\/strong><\/span> Jean-Fran\u00e7ois Richard<\/span><\/em>
    \nTerminotix – Introducing Asling to Terminotix<\/strong><\/a> (23 slides)<\/span><\/td>\n<\/tr>\n
    9:20<\/strong><\/span><\/td>\nSilver Sponsor: memoQ<\/strong><\/span> Peter Reynolds<\/span><\/em>
    \nmemoQ – Augmented-translation<\/strong><\/a> (6 slides)<\/span><\/td>\n<\/tr>\n
    9:30<\/strong><\/span><\/td>\nSilver Sponsor: televic<\/strong><\/span> Dicken Minta and Bert Wylin<\/span><\/em>
    \n Televic – We really listen<\/strong><\/a> (18 slides)<\/span><\/td>\n<\/tr>\n
    9:40<\/strong><\/span><\/td>\nSilver Sponsor: Wordbee<\/strong><\/span> Jaime Ochoa<\/span><\/em>
    \nWordbee – Intro for Asling41<\/a><\/span><\/strong> (7 slides – as presented)<\/span>
    \n<\/span><\/strong>    Wordbee – Presentation<\/strong><\/a> <\/span><\/span>(29 slides – extended presentation)<\/span><\/span><\/td>\n<\/tr>\n
    9:45<\/strong><\/span><\/td>\nKeynote speaker<\/strong><\/span>
    <\/span>Jochen Hummel (Coreon)<\/div>

    \"\"Jochen Humm<\/strong><\/span>el<\/strong> is co-founder and CEO of Coreon, the leading SaaS solution for multilingual knowledge systems. He is CEO of ESTeam AB, a provider of language technology and semantic solutions to EU organisations and corporations.\u00a0<\/span>He serves as vice-chairman of LT-Innovate, the Forum for Europe’s Language Technology Industry. He has a software development background and had grown his first company, TRADOS, to the world leader in translation memory and terminology software.\u00a0<\/span>In 2006 he founded Metaversum, the inventor of the virtual online world Twinity and was its CEO until 2010.\u00a0<\/span>He is a well-known, internationally respected software executive and serial entrepreneur. He serves on boards and is mentor\/angel for several start-ups in Berlin.<\/span><\/p>\n<\/div><\/div>

    <\/span>How to Unlock Machine Translation (Abstract)<\/div>

    For decades the basic architecture of Computer Assisted Translation (CAT) has been left unchanged. The advances in Neural Machine Translation (NMT) have now made the whole product category obsolete. While translation service providers pitch the concept of \u201caugmented translation\u201d to preserve their established way of operations, NMT is achieving \u201chuman parity\u201d. That changes everything. But only if actors, tools, workflow, and business models are remodeled. When done right, human talent creates multilingual knowledge, disruptive workflows offer incredible opportunities for new players, and Language becomes the key asset for data-driven organizations.<\/span><\/p>\n<\/div><\/div><\/span>How to Unlock NMT<\/strong><\/a> (30 slides)<\/span><\/td>\n<\/tr>\n

    10:45<\/strong><\/span><\/td>\n
    <\/span>Emmanuelle Esperan\u00e7a-Rodier, Francis Brunet-Manquat and Sophia Eady (Univ. of Grenoble-Alpes)<\/div>
    <\/div><\/div>
    <\/span>Accol\u00e9: A Collaborative Platform of Error Annotation for Aligned Corpora (Abstract)<\/div>

    This article presents a platform, named ACCOL\u00c9, for the collaborative annotation of translation errors. <\/span><\/p>\n

    ACCOL\u00c9 offers a range of services that allow simplified management of corpora and typologies of errors, annotation of effective errors, collaboration during annotation, and finally different kinds of search in corpora. ACCOL\u00c9 allows the annotation of translation errors according to built-in error typologies, Vilar’s typology or DQF-MQM or uploaded ones, on several corpora of different texts, translated by different Statistical or Neural Machine Translation systems, as well as processing the annotated corpora created in order to look for typical error models and patterns, related to a specific MT system. <\/span><\/p>\n

    The collaboration feature also gives the possibility to detect any misleading interpretation of an error type among the annotators. ACCOL\u00c9 currently provides 15 corpora, 7 projects of 201,474 words and 18,301 annotations that we will describe in the final paper. Eventually, we will implement the semi-automatic propagation of found patterns on other corpora to enlarge the scope of linguistic studies, thus providing to the community a wide range of error annotated bilingual parallel corpora.<\/span><\/p>\n<\/div><\/div><\/span> Accol\u00e9: A Collaborative Platform of Error Annotation for Aligned Corpora<\/strong><\/a> (26 slides)<\/span><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n\n\n\n\n\n\n
    Day 2<\/strong><\/span><\/td>\n 2nd<\/sup> morning Session<\/strong> – Co-chairs: Jo\u00e3o Esteves-Ferreira<\/strong> and Mar\u00eda Recort Ruiz<\/strong><\/span><\/td>\n<\/tr>\n
    11:45<\/strong><\/span><\/td>\n
    <\/span>Sabrina Girletti, Pierrette Bouillon, Martina Bellodi and Philipp Ursprung (Univ. of Geneva)<\/div>

    \"\"Sabrina Girletti<\/strong> is a PhD student at the Translation Technology Department of the Faculty of Translation and Interpreting (FTI) at the University of Geneva, where she contributes to postgraduate courses in machine translation and post-editing. Her research interests include post-editing approaches and human factors in machine translation. As a young language technology consultant, she also collaborates with Suissetra, the Swiss association for translation technology promotion. She is currently involved in projects testing the implementation of machine translation at several corporate language service departments in Switzerland. Sabrina holds a master\u2019s degree in Translation with a specialisation in Translation Technology from the University of Geneva and a bachelor\u2019s degree in Linguistic and Cultural Mediation from the University of Naples L\u2019Orientale.<\/span><\/p>\n

    \"\"Pierrette Bouillon<\/strong> has been Professor at the FTI, University of Geneva, since 2007. She is currently Director of the Department of Translation Technology (referred to by its French acronym TIM) and Dean of the Faculty. She has numerous publications in computational linguistics and natural language processing, particularly within lexical semantics (Generative lexicon theory), speech-to-speech machine translation for limited domains and, more recently, pre-editing and post-editing. Between 2012 and 2015, she coordinated the European ACCEPT project (Automated Community Content Editing PorTal). At present, she co-coordinates the new Swiss Research Center for Barrier-free communication with the Zurich University of Applied Sciences, and the project BabelDr with the HUG (Geneva University Hospitals). She also takes part in the new COST network EnetCollect: European Network for Combining Language Learning with Crowdsourcing Techniques.<\/span><\/p>\n

    \"\"Martina Bellodi<\/strong> graduated from the University of Bologna in 2003 and began her career as a freelance translator. In 2009 she started working as an in-house translator at Swiss Post Language Services. She was promoted to Head Translator in 2011 and to Deputy Head of Language Services in 2012. Since 2014 Martina has been in charge of Language Services\u2019 operational and strategic management. She holds an EMBA degree from the University of Applied Sciences and Arts of Southern Switzerland (SUPSI) and has appeared as a keynote speaker at several industry conferences (tcworld Stuttgart, LQA Symposium Zurich, XTM Live Amsterdam).<\/span><\/p>\n

    \"\"Philipp Ursprung<\/strong> holds a degree in Translation and translation technology from the University of Surrey (UK). Before joining Swiss Post as language technology specialist in 2018, he has worked in the localization industry for more than 10 years in different positions with both language services providers and corporate language services departments, where he focused on project management and the introduction of TMS and MT systems and optimization of translation processes and workflows.<\/span><\/p>\n<\/div><\/div><\/span>

    <\/span>Preferences of End-Users for Raw and Post-Edited NMT in a Business Environment (Abstract)<\/div>

    The in-house Language Service at Swiss Post translates a wide variety of texts from and into German, French, Italian, and English. It has emerged from internal discussions over the years that the extensive hype around neural machine translation (NMT) and its improved fluency, in comparison with previous approaches, has led many of Swiss Post\u2019s employees to turn to freely available, generic MT systems to obtain quick, raw translations. <\/span><\/p>\n

    Before introducing NMT into their production workflow, Swiss Post\u2019s Language Service decided to carry out a study to assess whether their customers (Swiss Post employees, who are also end-users of translations produced by the Language Service) would rate post-edited NMT more highly than raw NMT for both a customized and a generic MT engine (DeepL). Most importantly, the study also assessed whether the customers would be willing to pay for post-edited texts when made aware of some production metadata, such as data security and cost. This latter aspect could help determine whether the customers would still value the human intervention or whether they would rather accept a lower quality translation and associated risks if it means they can save on costs.<\/span><\/p>\n<\/div><\/div><\/span> Preferences of End-Users for Raw and Post-Edited NMT in a Business Environment<\/strong><\/a> (20 slides)<\/span><\/td>\n<\/tr>\n

    12:15<\/strong><\/span><\/td>\n
    <\/span>Caroline Champsaur (OECD)<\/div>

    \"\"<\/p>\n

    Caroline Champsaur<\/strong> has been working at the OECD for almost 20 years as the Head of the Reference and Terminology Unit (Translation Division). Over the years, she led the change from paper to digital. As a Counsellor for Digital, she also manages projects on Terminology and Machine Translation and participates to several projects of OECD\u2019s Digital Strategy.<\/p>\n

    She holds a PhD in computational linguistics and a Master\u2019s Degree in computer science (University of Paris 7, France), as well as a Master\u2019s Degree in German Language and Literature (University of Paris 7, France). She also studied Artificial Intelligence (Aachen University of Technology, Germany), German Language (Westf\u00e4lische Wilhelms-Universit\u00e4t M\u00fcnster, Germany) and French as a Foreign Language (University of Paris 7, France).<\/p>\n

     <\/p>\n<\/div><\/div><\/span>

    <\/span>OECD Neural Machine Translation Pilot Project: Methodology and Results (Abstract)<\/div>

    The OECD (Organisation for Economic Co-operation and Development) has launched a Digital Strategy in order to maximize the business benefits of digital initiatives. In 2018, one of the Translation Division\u2019s contributions to this program was the development of a Neural Machine Translation (NMT) system in collaboration with the WIPO (World Intellectual Property Organization). The objectives of this Pilot project was to explore new sources of efficiency gains and to extend the coverage of OECD content in both official languages (English and French).<\/span><\/p>\n

    This workshop will explain what has been achieved with this project. It will describe the methodology chosen to assess the quality of the sentences translated automatically and share the results and the lessons learned. Hopefully, it should help the participants decide how to take advantage of this new technology.<\/span><\/p>\n<\/div><\/div><\/span>OECD Neural Machine Translation Pilot Project: Methodology and Results<\/strong><\/a> (26 slides)<\/span><\/td>\n<\/tr>\n

    12:45<\/strong><\/span><\/td>\n
    <\/span>Ana-Luz Diaz and Simone Maier (University of Applied Sciences W\u00fcrzburg-Schweinfurt)<\/div>

    \"\"Ana-Luz Diaz<\/strong> M.A. in Translation and Interpreting (ES-EN-DE) from the University of Granada, M.A. in Journalism, from the University CEU Madrid.<\/p>\n

    Lecturer in Specialised Translation (DE>ES), Intercultural Communication and CAT Tools at FHWS University in Germany.<\/p>\n

    \"\"<\/p>\n

    Simone Maier<\/strong>, B.A. in Translation Studies from Heidelberg University, M.A. in Specialised Translation (DE-EN-ES), worked for several years as project manager and in-house translator.
    \nLecturer in Specialised Translation (EN>DE) for Software Localization and CAT Tools at FHWS University in Germany.<\/p>\n<\/div><\/div><\/span>

    <\/span>Short talk: Machine Translation vs. Human Translation: An Analysis of the Use and Impact of Pre-Editing in a Variety of Text Types (Abstract)<\/div>

    Post-editing has become a regular part of the machine translation process, and many translators are already specialised in providing this service. Post-editing is, however, not the only way to improve the outcome of machine translation. An alternative is the pre-editing of a text prior to the utilisation of machine translation software. The poster describes the results of an independent study project in a course for translation students working with the language pairs English-German and English-Spanish. The task was to analyse specific problems of MT with regard to text type and terminology, with a view to discovering how pre-editing a text can improve the result of MT. The participants first produced a human translation of a text they had chosen themselves. They then carried out a translation using DeepL and assessed the problems of the resulting texts. In order to reduce mistakes and to achieve a better machine-translated version, they pre-edited the source texts and compared the revised outcome to the previous versions. <\/span><\/p>\n

    Overall, it can be said that in many cases the translation by DeepL was surprisingly good and could be further improved by pre-editing. Some problems could easily be solved by pre-editing. This was achieved in particular by replacing terms that could have several meanings by terms which only imply the desired meaning, and that sentences in which the references were not clear reformulated so that the references were clear. This was in line with the general rules for pre-editing, that ambiguous terms should be avoided, references should be clearly identifiable, and complex sentences should be simplified. The translation results that were achieved by pre-editing the texts were surprisingly good in some cases, but there are certain problems that cannot be solved without post-editing. The inconsistent use of terms, for example, is an almost unmanageable problem. A human translator is therefore still essential for a good translation.<\/span><\/p>\n<\/div><\/div><\/span>Machine Translation vs. Human Translation: An Analysis of the Use and Impact of Pre-Editing in a Variety of Text Types<\/strong><\/a> (24 slides)<\/span><\/td>\n<\/tr>\n

    13:00<\/strong><\/span><\/td>\n
    <\/span>Argelia Pe\u00f1a Aguilar (University of Ottawa)<\/div>

    \"\"Argelia Pe\u00f1a<\/span><\/strong> Aguilar<\/strong> has been an Associate professor at the University of Quintana Roo (Mexico) for eleven years.\u00a0 She has taught English Language and Translation\/Interpreting courses from English into Spanish in the Language and Education Department.\u00a0 She is currently studying for a PhD in Translation Studies at the University of Ottawa (second year), and her research interests revolve around translation technologies training, and feminist translation studies.<\/span><\/p>\n<\/div><\/div><\/span>

    <\/span>Short talk: Usefulness of Translation Technology Training from Mexican Universities (Abstract)<\/div>

    In a study done in 2018 it was reported that few professors teach technology in few translation courses in Mexico. Some reasons for this were that instructors had not been well trained in their academic programs when they were students, or they lacked a more comprehensive knowledge of these technologies (Pe\u00f1a, 2018). Effective training was not possible for most of these instructors as students and they seem to be reproducing similar learning insufficiencies with future translators. Because of this, another survey-based project was devised to identify the use that professionals who graduated from Mexican translation programs are making of translation technologies. <\/span><\/p>\n

    How has their educational background affected their disposition towards the use of translation technologies? Some results indicate that professional translators do not resort to the use of \u201ccore\u201d translation technologies very often, but do use other electronic resources useful for accomplishing their tasks. One in two translators thinks their income has increased due to their technology knowledge, and they learn about these technologies on their own. Professional translators think they could have learned about Translation Environment Tools ((TEnTs) at university (and they wished they had), but university instructors are still not teaching these technologies as much. So there is a need reported by a few professionals, but not being dealt by some university programs. <\/span><\/p>\n

    Keywords: translation technologies, translation training, TEnTs, translation environment tools, computer-aided translation, CAT, Mexico<\/span><\/p>\n

    Pe\u00f1a, A. (2018). Use of technologies in Mexican translation programs. Unpublished manuscript.<\/span><\/p>\n<\/div><\/div><\/span>Usefulness of Translation Technology Training from Mexican Universities<\/strong><\/a> (10 slides)<\/span><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n\n\n\n\n\n
    Day 2<\/strong><\/span><\/td>\n 1st<\/sup> afternoon Session <\/strong>– Chair: Olaf-Michael Stefanov<\/strong><\/span><\/td>\n<\/tr>\n
    14:15<\/strong><\/span><\/td>\n
    <\/span>Josep Bonet (WTO)<\/div>

    \"\"Josep Bonet<\/strong> was not meant to be a translator, but rather a chemist. Something went wrong, though, and finished by spending almost 30 years in the Directorate-General for Translation of the European Commission. He played most the roles available, translator, help desk officer, information officer, communication officer, manager of units concerned with translation, IT, language technologies and terminology, learning & development, knowledge management, etc. He chaired the JIAMCATT forum, where international organisations discuss language technology. Presently he is Director of the languages, Documentation and Information Management Division of the World Trade Organization. He pretends that abandoning active translation allowed the average quality of translation output to increase substantially.
    \n<\/span><\/p>\n<\/div><\/div>

    <\/span>Innovation in the International Organisation: Can We Do Better? (Abstract)<\/div>

    The modern economy is all about innovation, disruption, shifting paradigms, accelerating the pace of change. International Organizations may be perceived as not following this trend. Is this true? How do they react to changes in their environment? This presentation will give some insights, based on experience in two such organizations, one very large and another of medium size.<\/span><\/p>\n<\/div><\/div><\/span>Innovation in the International Organisation: Can We Do Better?<\/strong><\/a> (26 slides)<\/span><\/td>\n<\/tr>\n

    14:45<\/strong><\/span><\/td>\n
    <\/span>Emmanuelle Esperan\u00e7a-Rodier and Caroline Rossi (Univ. of Grenoble-Alpes)<\/div>

    \"\"Emmanuelle Esperan\u00e7a-Rodier<\/strong> is a lecturer at Univ. Grenoble Alpes (UGA), France, where she teaches English for Specific Purposes, and a member of the Laboratoire d’Informatique de Grenoble (LIG). After defending a PhD in computational linguistics, on “Cr\u00e9ation d’un Diagnostique G\u00e9n\u00e9rique de Langues Contr\u00f4l\u00e9es, avec application particuli\u00e8re \u00e0 l’Anglais Simplifi\u00e9”, she worked as a post-editor in a translation agency. Back at University, she participated in IWSLT and WMT evaluation campaigns, as well as in several LIG projects. She now works on the evaluation of MT systems based on competences and focused on tasks, translation error analysis and multilinguism.<\/span><\/p>\n

    \"\"Caroline Rossi<\/strong> is a lecturer in the Applied Modern Languages department at Univ. Grenoble Alpes, where she teaches English and translation. She is a member of the Multilingual Research Group on Specialized Translation (GREMUTS) within ILCEA4\u00a0 (Institut des Langues et Cultures d\u2019Europe, Am\u00e9rique, Afrique, Asie, Australie). Her current research focus is on integrating critical skills and understanding of both statistical and neural machine translation in translator training.<\/span><\/p>\n<\/div><\/div><\/span>

    <\/span>Time is Everything: A Comparative Study of Human Evaluation of SMT vs. NMT (abstract)<\/div>

    Translation process research has developed tools to gather and analyse empirical data, but while a variety of measures have proved useful and reliable to measure post-edit machine translation effort (see e.g. Vieira 2016 : 42), translation processes are seldom considered when assessing the relevance of a given Machine translation post-editing scenario. Our study seeks to determine the impact of including MTPE in the evaluation process. We selected adequacy and fluency ratings. Based on two distinct experimental conditions, we then compared the ratings produced without performing PE and those produced immediately after a light PE process. Inter-rater reliability was assessed for each segment in each text (N=55) using Fleiss\u2019 kappa for adequacy and fluency scores, and an intraclass correlation coefficient (Vieira 2016 : 52) for temporal measures. While the reliability of the measures collected without PE was low, the measures collected in PET were for the most part homogeneous. Qualitative analyses of the problematic segments, as evidenced by both kappa and intraclass correlation coefficients, showed strong Spearman’s correlations, whether positive or negative, between temporal measures and all the other metrics for NMT but weakest ones for SMT. Based on these results, we discuss the advantages and risks of NMTPE.<\/span><\/p>\n<\/div><\/div><\/span>Time is Everything: A Comparative Study of Human Evaluation of SMT vs. NMT<\/strong><\/a> (23 slides)<\/span><\/td>\n<\/tr>\n

    15:15<\/strong><\/span><\/td>\n
    <\/span>Luc\u00eda Guerrero and Kirill Soloviev (CPSL and ContentQuo)<\/div>

    \"\"Luc\u00eda Guerrero<\/span><\/strong> is a Machine Translation Specialist at CPSL, a linguistic services provider based in Spain with presence in Germany, the UK and the US. The range of services includes translation, software and web localization, multilingual SEO, interpreting, multimedia and e-learning in all major Western and Eastern European, Scandinavian, Asian and Middle-Eastern languages. Luc\u00eda is also part of the collaborative teaching staff at the Universitat Oberta de Catalunya. Having worked in the translation industry since 1998, she has also been a senior Translation and Localization Project Manager specialized in international institutions, has managed localization projects for Apple Computer and has translated children\u2019s and art books.<\/span><\/p>\n

    \"\"<\/p>\n

    Kirill Soloviev<\/strong> is the Co-Founder & CEO at ContentQuo, an Estonian tech startup helping Global Top-10 LSPs, enterprise loc teams, and government agencies reduce translation quality risk, improve vendor performance, and boost MT quality at any scale, regardless of their TMS. During his 16-year industry career, Kirill served in diverse buyer-side & vendor-side roles, most recently as Global Director of Localization at Acronis, a $150M data protection and disaster recovery software company. Kirill also co-organises Localization Unconference in Tallinn, collaborates with TAUS, and loves consulting both new and seasoned localization pros about their careers.<\/p>\n<\/div><\/div><\/span>

    <\/span>Machine Translation Evaluation at CPSL with ContentQuo (Abstract)<\/div>

    Evaluating the performance of an MT system with new content – that is, MT performance prediction – is one of the most challenging aspects of MT, mainly because lack of reference translations does not allow using automatic metrics. Additionally, human evaluation can be expensive and time-consuming. As an LSP, at CPSL we deal with hundreds of translation requests daily and must choose the most appropriate workflow for our customers in a timely manner. That\u2019s why we needed a fast, reliable and cost-effective solution allowing us to find out if a given MT system is suitable for specific content. After trying different methods and tools, we chose the solution provided by ContentQuo, a translation quality management platform, based on the widely accepted Adequacy-Fluency methodology for MT evaluation. In our presentation we will introduce you to the challenges of MT quality evaluation and how we address them with ContentQuo.<\/span><\/p>\n<\/div><\/div><\/span>Machine Translation Evaluation at CPSL with ContentQuo<\/strong><\/a> (15 slides)<\/span><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

    <\/span>In alphabetical order of first author<\/div>
    \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
    Argelia Pe\u00f1a Aguilar (University of Ottawa)<\/span><\/td>\nShort talk: Usefulness of Translation Technology Training from Mexican Universities<\/span><\/td>\nUsefulness of Translation Technology Training from Mexican Universities<\/strong><\/a> (10 slides)<\/span><\/td>\n<\/tr>\n
    Josep Bonet (WTO)<\/span><\/td>\nInnovation in the International Organisation: Can We Do Better?<\/span><\/td>\n]<\/span>Innovation in the International Organisation: Can We Do Better?<\/strong><\/a> (26 slides)<\/span><\/td>\n<\/tr>\n
    Caroline Champsaur (OECD)<\/span><\/td>\nOECD Neural Machine Translation Pilot Project: Methodology and Results<\/span><\/td>\nOECD Neural Machine Translation Pilot Project: Methodology and Results<\/strong><\/a> (26 slides)<\/span><\/td>\n<\/tr>\n
    Denis Dechandon (EPO), Maria Recort Ruiz (ILO) and Aniko Gerencser (EPO)<\/span><\/td>\nTerminology: Towards a Systematic Integration of Semantics and Metadata<\/span><\/td>\nTerminology: Towards a Systematic Integration of Semantics and Metadata<\/a><\/strong> (21 slides – as presented)<\/span>
    \n     Terminology: Towards a Systematic Integration of Semantics and Metadata<\/strong><\/a> (31 slides – extended slide set)<\/span><\/td>\n<\/tr>\n
    Ana-Luz Diaz and Simone Maier (University of Applied Sciences W\u00fcrzburg-Schweinfurt)<\/span><\/td>\nShort talk: Machine Translation vs. Human Translation: An Analysis of the Use and Impact of Pre-Editing in a Variety of Text Types<\/span><\/td>\nMachine Translation vs. Human Translation: An Analysis of the Use and Impact of Pre-Editing in a Variety of Text Types<\/strong><\/a> (24 slides)<\/span><\/td>\n<\/tr>\n
    Emmanuelle Esperan\u00e7a-Rodier, Francis Brunet-Manquat and Sophia Eady (Univ. of Grenoble-Alpes)<\/span><\/td>\nAccol\u00e9: A Collaborative Platform of Error Annotation for Aligned Corpora<\/span><\/td>\nAccol\u00e9: A Collaborative Platform of Error Annotation for Aligned Corpora<\/strong><\/a> (26 slides)<\/span><\/td>\n<\/tr>\n
    Emmanuelle Esperan\u00e7a-Rodier and Caroline Rossi (Univ. of Grenoble-Alpes)<\/span><\/td>\nTime is Everything: A Comparative Study of Human Evaluation of SMT vs. NMT<\/span><\/td>\nTime is Everything: A Comparative Study of Human Evaluation of SMT vs. NMT<\/strong><\/a> (23 slides)<\/span><\/td>\n<\/tr>\n
    Sabrina Girletti, Pierrette Bouillon, Martina Bellodi and Philipp Ursprung (Univ. of Geneva)<\/span><\/td>\nPreferences of End-Users for Raw and Post-Edited NMT in a Business Environment<\/span><\/td>\nPreferences of End-Users for Raw and Post-Edited NMT in a Business Environment<\/strong><\/a> (20 slides)<\/span><\/td>\n<\/tr>\n
    Christopher Gledhill and Maria Zimina (Univ. Paris-Diderot)<\/span><\/td>\nThe Impact of Machine Translation on a Masters Course in Web Translation: From Disrupted Practice to a Qualitative Translation\/Revision Workflow<\/span><\/td>\nThe Impact of MT on a Masters Course in Web Translation<\/strong><\/a> (26 Slides)<\/span><\/td>\n<\/tr>\n
    Luc\u00eda Guerrero and Kirill Soloviev (CPSL and ContentQuo)<\/span><\/td>\nMachine Translation Evaluation at CPSL with ContentQuo<\/span><\/td>\nMachine Translation Evaluation at CPSL with ContentQuo<\/strong><\/a> (15 slides)<\/span><\/td>\n<\/tr>\n
    Marion Kaczmarek and Michael Filhol (LIMSI)<\/span><\/td>\nAssisting Sign Language Translation: what Interface Given the Lack of Written Form and the Spatial Grammar?<\/span><\/td>\nAssisting Sign Language Translation<\/strong><\/a> (13 slides)<\/span><\/td>\n<\/tr>\n
    Rodolfo Maslias (European Parliament)<\/span><\/td>\nNew Audiences for EU Terminology<\/span><\/td>\nNew Audiences for Terminology<\/strong><\/a> (44 slides)<\/span><\/td>\n<\/tr>\n
    Jean-Francois Richard (Terminotix)<\/span><\/td>\nTerminology Extraction as a Tool for MT Output Assessment and Improvement<\/span><\/td>\nTerminology extraction as a tool for MT output<\/strong><\/a> (6 slides)<\/span><\/td>\n<\/tr>\n
    Ayla Rigouts Terryn, Lieve Macken, Els Lefever, Robert Vander Stichele, Koen Vanneste and Joost Buysschaert (Uni. of Ghent)<\/span><\/td>\nPilot Study on Medical Translations in Lay Language: Post-Editing by Language Specialists, Domain Specialists or Both?<\/span><\/td>\nPilot Study on Medical Translations in Lay Language<\/strong><\/a> (32 slides)<\/span><\/td>\n<\/tr>\n
    Margita \u0160o\u0161tari\u0107, Nata\u0161a Pavlovi\u0107 and Filip Boltu\u017ei\u0107 (Univ. of Zagreb)<\/span><\/td>\nDomain Adaptation for Machine Translation Involving a Low-Resource Language: Google AutoML vs. Form-Scratch NMT System<\/span><\/td>\nDomain-adapted MT<\/strong><\/a> (28 slides)<\/span><\/td>\n<\/tr>\n
    Maria Stasimioti and Vilelmini Sosoni (Ionian University)<\/span><\/td>\nUndergraduate Translation Students\u2019 Performance and Attitude towards Machine Translation and Post-editing: Does Training Play a Role?<\/span><\/td>\nUndergraduate Translation Students\u2019 Performance and Attitude towards Machine Translation and Post-editing<\/strong><\/a> (33 slides)<\/span><\/td>\n<\/tr>\n
    Ale\u0161 Tamchyna (Memsource)<\/span><\/td>\nApplying AI to NT and MT<\/span><\/td>\nApplying AI to NT and MT<\/strong><\/a> (24 slides)<\/span><\/td>\n<\/tr>\n
    Andrzej Zydro\u0144 (XTM Int.)<\/span><\/td>\nDe-demonizing AI<\/span><\/td>\nDe-demonizing AI<\/strong><\/a> (30 slides)<\/span><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div><\/div>\n","protected":false},"excerpt":{"rendered":"

    X Presentations became available here shortly after they were presented The slides of all presentations are available for download; see below 21 November go to 22 November Day 1 1st morning Session – Chair: Jo\u00e3o Esteves-Ferreira \u00a0 Sponsors’ Thought Leadership Talks 9.10 Gold Sponsor: STAR Judith Klein Are we ready for the (M)Translation Future? (21 … Read more<\/a><\/p>\n","protected":false},"author":3,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"generate_page_header":"","footnotes":""},"class_list":{"0":"post-2078","1":"page","2":"type-page","3":"status-publish","5":"no-featured-image-padding"},"_links":{"self":[{"href":"https:\/\/www.asling.org\/tc41\/index.php?rest_route=\/wp\/v2\/pages\/2078","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.asling.org\/tc41\/index.php?rest_route=\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/www.asling.org\/tc41\/index.php?rest_route=\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/www.asling.org\/tc41\/index.php?rest_route=\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/www.asling.org\/tc41\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=2078"}],"version-history":[{"count":0,"href":"https:\/\/www.asling.org\/tc41\/index.php?rest_route=\/wp\/v2\/pages\/2078\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.asling.org\/tc41\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=2078"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}