Dr Joss Moorkens, Dublin City University.

Digital Taylorism in the Translation Industry

23 July 2021

Abstract:

Translators have worked with the assistance of computers for many years, usually translating whole texts, divided into segments but in sequential order. In order to maximise efficiency and inspired by similar moves in the tech industry and predictions for Industry 4.0, large translation companies have begun to break tasks down into smaller chunks and to rigidly define and monitor translation processes. This is particularly true of platform-mediated work, highly collaborative workflows, and multimedia work that requires near-live turnaround times. This article considers such workflows in the context of measures of job satisfaction and discussion of sustainable work systems, proposing that companies prioritise long-term returns and attempt to balance the needs of all stakeholders in a translation process. Translators and translator trainers also have a role to play in achieving this balance.

Bio:

Joss Moorkens is an Associate Professor and Chair of postgraduate translation programmes at the School of Applied Language and Intercultural Studies at Dublin City University. He is also a Funded Investigator with the ADAPT Centre and a member the Centre for Translation and Textual Studies. He has authored over 50 journal articles, book chapters, and conference papers on translation technology, user interaction with and evaluation of machine translation, translator precarity, and translation ethics. He is General Coeditor of the journal Translation Spaces with Prof. Dorothy Kenny, and coedited the book ‘Translation Quality Assessment: From Principles to Practice’, published in 2018 by Springer, and special issues of Machine Translation (2019) and Translation Spaces (2020). He leads the Technology working group (with Prof. Tomas Svoboda of Charles University) as a board member of the European Masters in Translation network and sits on the advisory board of the Journal of Specialised Translation.

Prof. Barry Olsen, The Middlebury Institute of International Studies

RSI has taken the world by storm. So, what have we learned and where do we go from here?

16 July 2021

Abstract

No one could have foreseen the effects of the COVID-19 pandemic on the interpreting profession or its accompanying effects on the adoption rate of remote simultaneous interpretation (RSI) all over the world. In a question of weeks, international organizations, national governments, non-governmental organizations, and private corporations were meeting, negotiating, and conducting business online at a scale never seen before, often in multiple languages. But this abrupt adoption of web conferencing with RSI was not entirely smooth or without its challenges. We are now at a stage where we can compile a list of lessons learned during this unprecedented shift in professional practice and turn our sights toward the future to address the new digital world of multilingual communication and interpretation technology’s place in it.  This presentation will share some of those lessons learned and some thoughts about what the future of RSI may hold.

Bio

Barry Slaughter Olsen is a veteran conference interpreter and technophile with over twenty-five years of experience interpreting, training interpreters, and organizing language services. He is a professor at the Middlebury Institute of International Studies at Monterey (MIIS) and the Vice-President of Client Success at KUDO, a multilingual web conferencing platform. He was co-president of InterpretAmerica from 2009 to 2020. A pioneer in the field of remote simultaneous interpretation (RSI), he is co-inventor on two patents on RSI technologies. He is a member of the International Association of Conference Interpreters (AIIC). Barry has been interviewed numerous times by international media (CNN, CBC, MSNBC, NPR, and PBS) about interpreting and translation. For updates on interpreting, technology, and training, follow him on Twitter @ProfessorOlsen.

Prof Ruslan Mitkov, University of Wolverhampton

What does the future hold for humans, computers, translators, and interpreters?

A non-clairvoyant’s view.

22 July  2021

(60-min introduction to Natural Language Processing)

Abstract:  Computers are ubiquitous – they can be found and used everywhere. But how good are computers at understanding, producing, and translating natural languages? In other words, what is the level of their linguistic intelligence? This presentation will examine the linguistic intelligence of computers and will ask the question of how far advances in Artificial Intelligence (AI) can go. Illustrations will be provided through key applications addressing parts of the translation process such as machine translation and translation memory systems and the challenges ahead will be commented on …

The presentation begins with a brief historical flashback, plotting the timeline of the linguistic intelligence of computers against that of humans. It then gives another snapshot in time depicting early work on Machine Translation. Over the last 20 years, as will be discussed in the presentation, advances in Natural Language Processing (NLP) have significantly increased the linguistic intelligence of computers but this intelligence still lags behind that of humans.

The presentation will go on to explain why it is so difficult for computers to understand, translate and, in general, to process natural languages; it is a steep road, and a long and winding one, for both computers and researchers. The talk will briefly present well-established NLP techniques that computers use when ‘learning’ to speak our languages, including initial rule-based and knowledge-based methods and more recent machine learning as well as deep learning methods, which are regarded as highly promising. A selection of Natural Language Processing applications will be outlined after that. In particular, the talk will look at the recent advances in Machine Translation and will assess the claims that Neural Machine Translation has reached parity with human translation.

The speaker will express his views on the potential of MT, and the latest research on ‘intelligent’ Translation Memory systems will be outlined along with expected developments. The future of Interpreting Technology and its impact on interpreters will also be touched on.

I am no clairvoyant, but during my plenary talks I am often asked to predict how far computers will go in their ability to learn and translate language. At the end of my presentation I shall share with you my predictions and, in general, my vision for the future of translation and interpreting technologies. These predictions, though tentative, will be relevant to the impact that AI advances can have on the work of translators and interpreters in the future.



Speaker’s bio: Prof Dr Ruslan Mitkov has been working in Natural Language Processing (NLP), Computational Linguistics, Corpus Linguistics, Machine Translation, Translation Technology and related areas since the early 1980s. Whereas Prof Mitkov is best known for his seminal contributions to the areas of anaphora resolution and automatic generation of multiple-choice tests, his extensively cited research (more than 250 publications including 16 books, 32 journal articles and 37 book chapters) also covers topics such as machine translation, translation memory and translation technology in general, bilingual term extraction, automatic identification of cognates and false friends, natural language generation, automatic summarisation, computer-aided language processing, centering, evaluation, corpus annotation, NLP-driven corpus-based study of translation universals, text simplification, NLP for people with language disorders and more recently – computational phraseology. Mitkov is author of the monograph Anaphora resolution (Longman) and Editor of the most successful Oxford University Press Handbook – The Oxford Handbook of Computational Linguistics. Current prestigious projects include his role as Executive Editor of the Journal of Natural Language Engineering published by Cambridge University Press and Editor-in-Chief of the Natural Language Processing book series of John Benjamins publishers. Dr Mitkov is also working on the forthcoming Oxford Dictionary of Computational Linguistics (Oxford University Press, co-authored with Patrick Hanks) and the forthcoming second, substantially revised edition of the Oxford Handbook of Computational Linguistics.

Prof Mitkov has been invited as a keynote speaker at a number of international conferences. He has acted as Programme Chair of various international conferences on Natural Language Processing (NLP), Machine Translation, Translation Technology, Translation Studies, Corpus Linguistics and Anaphora Resolution. He is asked on a regular basis to review for leading international funding bodies and organisations and to act as a referee for applications for Professorships both in North America and Europe. Ruslan Mitkov is regularly asked to review for leading journals, publishers and conferences and serve as a member of Programme Committees or Editorial Boards. Prof Mitkov has been an external examiner of many doctoral theses and curricula in the UK and abroad, including Master’s programmes related to NLP, Translation and Translation Technology. Dr Mitkov has considerable external funding to his credit (more than є 20,000,000) and is currently acting as Principal Investigator of several large projects, some of which are funded by UK research councils, by the EC as well as by companies and users from the UK and USA.

Ruslan Mitkov received his MSc from the Humboldt University in Berlin, his PhD from the Technical University in Dresden and worked as a Research Professor at the Institute of Mathematics, Bulgarian Academy of Sciences, Sofia. Mitkov is Professor of Computational Linguistics and Language Engineering at the University of Wolverhampton which he joined in 1995 and where he set up the Research Group in Computational Linguistics. His Research Group has emerged as an internationally leading unit in applied Natural Language Processing and members of the group have won awards in different NLP/shared-task competitions. In addition to being Head of the Research Group in Computational Linguistics, Prof Mitkov is also Director of the Research Institute in Information and Language Processing and Director of the Responsible Digital Humanities Lab. The Research Institute consists of the Research Group in Computational Linguistics and the Research Group in Statistical Cybermetrics, which is another top performer internationally. Ruslan Mitkov is Vice President of ASLING, an international Association for promoting Language Technology. Dr Mitkov is a Fellow of the Alexander von Humboldt Foundation, Germany, was a Marie Curie Fellow, Distinguished Visiting Professor at the University of Franche-Comté in Besançon, France and Distinguished Visiting Researcher at the University of Malaga, Spain; he also serves/has served as Vice-Chair for the prestigious EC funding programmes ‘Future and Emerging Technologies’ and ‘EIC Pathfinder Open’. In recognition of his outstanding professional/research achievements, Prof Mitkov was awarded the title of Doctor Honoris Causa at Plovdiv University in November 2011. At the end of October 2014 Dr Mitkov was also conferred Professor Honoris Causa at Veliko Tarnovo University.

Dr Ahmed Omer, XTM International

5 July 2021

Title: Computational Stylometry of Arabic Literature

Abstract:

The successful implementation of stylometric methods with English texts has motivated researchers who work with the Arabic texts to investigate whether they can use these methods in the Arabic language as well. Taking into account the different characteristics of the Arabic language, the main aim of my study is to investigate what are the most useful linguistic features to enable the authorship attribution task to be accomplished for Arabic texts. As well as using features derived from English studies of author attribution, I developed a number of feature sets derived from Arabic linguistic theory, namely Arud, Nazm and Wazn. The feature sets were compared on two corpora of travelogues, one in English and one in Arabic. The feature sets were examined in conjunction with agglomerative clustering methods and traditional machine learning classifiers including SVM, Naïve Bayes, and KNN, as well as a Deep Learning model implemented using the open source package Keras. The findings from this first part of the thesis were used to examine six real-life case studies from Arabic, two of Authorship Attribution, two on Author Profiling, and two on Authorship Verification. These case studies respectively were:

· Was Al-Qarni’s “Don’t Despair” plagiarised from Salwa?

· Did Abdu or Amin write certain key chapters of “Women’s Rights”?

· Were the “Hanging Poems” pre-Islamic or more recent?

· A study of the dialectology of Arabic speech.

· Was a box of posthumous texts by the Nobel prize winner Naguib Mahfouz indeed by him?

· Were some texts written by the Mediaeval scholar Al-Ghazali by him or by somebody else?

Bio:

Ahmed Omer has an M.Sc. in Computer Science from Napier University in Edinburgh and a Ph.D. in Computational Linguistics from the University of Wolverhampton. He is now working at XTM International as a Computational Linguistics Expert. The company is working in Machine Translation and they use the Inter-language vector space method. This interesting method has been used by Google and recently by Facebook to enforce their polices and to translate texts for customers in their platform.