Sharon O’Brien is Professor of Translation Studies in the School of Applied Language and Intercultural Studies, Dublin City University, Ireland, where she teaches translation technology, localisation, and research methods, among other topics. Since 2020, she is Associate Dean for Research at DCU’s Faculty of Humanities and Social Sciences. Previously, she was coordinator of an interdisciplinary and inter-sectoral Horizon 2020-funded project on translation in emergency and disaster settings – INTERACT, The International Network in Crisis Translation (2017-2020), a topic that she continues to research. She has been a funded investigator in the Science Foundation Ireland national research centre, ADAPT. Sharon has supervised twelve PhD students to date and has mentored several post-doctoral fellows.
Augmented Translation: New Trend, Future Trend or Just Trendy?
We have recently started to hear discussions about AI-driven “augmented translation”, with suggestions that this will represent a “profound technological shift that will affect all parties” (Lommel and DePalma, 2021). In this talk I will examine what is generally meant by “augmented translation”, whether this is a current trend, or a trend we can expect in the (near?) future. Or, is it just a trendy term for reconceptualising technology that already exists? Examining how “augmentation” has been viewed in the past and how it is being discussed and implemented in other areas will help us to consider its possibilities and limitations for translation. Finally, I will focus on the feasibility and desirability of “augmentation”, asking who might actually benefit from it, if it were achievable.
Lommel, Arle and Don DePalma (2021) “Augmented Translation: How Artificial Intelligence Drives Productivity and Efficiency for the Language Industry”. Common Sense Advisory Research.
Prof. Mikel L. Forcada is full professor of Computer Languages and Systems at the Universitat d’Alacant and he has also been president of the European Association for Machine Translation from 2015 to 2021. From the turn of the millennium, Prof. Forcada’s research interests have mainly focused on the field of translation technologies, but he has worked in fields as diverse as quantum chemistry, biotechnology, surface physics, machine learning (especially with neural networks) and automata theory. He is the author of more than 70 articles in international journals, papers in international conferences and book chapters, of which about 40 are about translation technologies. In 2004, after heading several publicly- and privately-funded projects on machine translation he started the free/open-source machine translation platform Apertium (with more than 40 language pairs) and the free/open-source software project Bitextor (which crawls Internet sites to harvest parallel corpora). He is also the co-founder of Prompsit Language Engineering (2006). Prof. Forcada has participated in the scientific committees of more than thirty international conferences and workshops. During 2009–2010 he was an ETS Walton Visiting Professor in the machine translation group at Dublin City University; during 2016–2017 he was visiting professor at Sheffield University and the University of Edinburgh.
Usage rights of language data in machine translation
Machine translation relies heavily on data. In rule-based machine translation, an engine performs the translation task using language resources such as dictionaries and grammar rules, usually written by experts, but sometimes learned from monolingual or bilingual texts. Automatic corpus-based (statistical and, more recently, neural) machine translation takes advantage of large amounts of bilingual (sentence-aligned) and monolingual texts, very often harvested using web-crawling techniques. Clearly, the machine translation software that exploits these data to translate is creative work that may be copyrighted, but this talk will focus on the data instead. Human work, and therefore the creative authorship of works, is present in all forms of machine translation data: monolingual text has been written, parallel text has been translated and aligned, and rules and dictionaries have been written by experts. Since it was conceived centuries ago, copyright protects the livelihoods of authors by regulating how copies of these data may be used and how the resulting works may be used and published, through instruments such as licences. Although the case for dictionaries and grammars as used in rule-based machine translation is reasonably clear, as they are written specifically for one language processing application or other, monolingual and parallel text, as used in machine translation, was not created with machine translation in mind, and this has led some authors to wonder whether authors and translators should obtain additional compensation for this unexpected use of their work to generate new “downstream” value. The conference provides an overview of the different data sources used in machine translation, discusses their authorship throughout the steps of creation, care and transformation of this data for use with machine translation, and touches on the types of implicit and explicit licensing schemes that apply to them and how they work. I will also discuss the controversy surrounding the use of published works to generate new, initially unforeseen value through translation technologies, and the different ways in which copyright issues are addressed in practice.
Marcello Federico is a Principal Applied Scientist at AWS AI Labs, USA, since 2018. At Amazon, he leads science teams working on automatic dubbing and machine translation. From 2002 to 2020, he has been lecturer and member of the ICT International Doctoral School’s Committee of the University of Trento. He was co-founder and scientific advisor of MateCat Srl (2014-2021), co-founder and former CEO of MMT Srl (2017-2018), the first company offering a real-time adaptive neural machine translation technology. His research expertise is in machine translation, spoken language translation, language modelling, information retrieval, and speech recognition. Since 2004, he is on the steering committee of the International Conference on Spoken Language Translation series. He served as editor-in-chief of the ACM Transactions on Audio, Speech and Language Processing; as associate editor for Foundations and Trends in Information Retrieval, and as senior editor for the IEEE/ACM Transactions on Audio, Speech, and Language Processing. He has been a board member of the Cross Lingual Information Forum and the European Association for Machine Translation (chair of EAMT 2012), founding officer of the ACL SIG on Machine Translation. He is currently President of the ACL SIG on Spoken Language Translation and associate editor of the Journal of Artificial Intelligence Research. He is a senior member of the IEEE and of the ACM.
Machine Translation using Context Information
Machine translation (MT) is typically formulated as the task of returning a correct translation for a single input sentence, and as a result, many alternative translations of a sentence can be considered as valid. However, human translations are mostly generated and consumed for specific communicative purposes and situations. Both verbal and social context provide important clues for generating appropriate translations, that are not only correct in terms of content but that also address the audience with the correct style and register. Thus an ideal MT system would be able to control language variations via context, such as the previous sentences, the characteristics of the speaker, the relationship between the speaker and the audience, the domain of discourse, the specific use case, and so on. The recent “neural revolution” brought us impressive improvements in MT quality but also a new paradigm to approach AI problems. However, if we look at how current state-of-the-art MT works in communicative situations where context plays an important role, we get the impression that we still have a long journey ahead. In this talk, I will present results of work carried out at AWS AI Labs to close this gap and enhance neural MT along different linguistic traits that depend on context information: discourse domain, translation style, verbosity and register. I will also discuss a challenging use case in which contextual information is extremely important: the translation of movie scripts.
Merit-Ene Ilja is a Translation Director in the Directorate-General forTranslation (DGT) of the European Commission. She oversees the operations of the Spanish, Irish, Croatian, Hungarian, Latvian, Dutch and Slovenian language departments. She is also in charge of the CAT environment and language technology applications in the DGT’s translation ecosystem.
A linguist by qualifications with an academic background, she worked as a lecturer at the University of Tartu from 1984 to 1995. She has extensive experience in translation management going back to 1995 when she worked for the Estonian Translation Centre. She managed the Centre to successfully deliver the translation of the acquis communautaire in the run up to Estonia’s accession to the EU.
She started her career in the EU institutions in 2004 when Estonia joined the EU. From 2004 to 2008, she managed the Estonian and Hungarian translation units in the General Secretariat of the Council of the EU. She has been working for European Commission since 2008 and fulfilled a range of management functions such as managing the Estonian, Romanian and Slovenian Language Departments and heading the unit responsible for professional and organisational development issues in DGT. In 2013 she became Director in the team of four Translation Directors in DGT
The power of people and technology in DGT’s translation ecosystem
In recent decades, rapid advances in language technology have been leaving a heavy imprint on the way legal translation is organized in both the private and public sectors.
The European Commission’s Directorate-General for Translation has been at the forefront of digital transformation in the translation profession in the public sector, introducing and developing eTranslation, its machine translation system, sponsored by the Connecting Europe Facility.
In her presentation Merit-Ene Ilja focuses on the language technology applications and tools used in the DGT’s translation ecosystem to support the human-in-the-loop translation workflow. Language technology is seen as a key component in DGT’s resource mix to enable effective and efficient translation of EU legislation and communication material. The use of benefits offered by technology is made possible thanks to staff and continued investment in their professional development. Today, DGT’s learning and development priorities for translation staff are centred around enhancing their digital proficiency, thematic knowledge and data management skills, vital for quality and efficiency in translation work.
Valter Mavrič is Director-General of the Translation Service (DG TRAD) at the European Parliament (since 2016), where he was previously acting Director-General (from 2014), Director (from 2010) and Head of the Slovenian Translation Unit (from 2004). With an MA in applied linguistics and further training in translation, interpretation, linguistics and management, he has a long experience as manager, translator, interpreter and teacher of languages. He works in Slovenian, Italian, English, French, and Croatian and is currently preparing a PhD in strategic communication.
The evolution of the role of translator in the European Parliament: all about becoming a versatile language professional
In my presentation I will illustrate how the translator in the European Parliament has evolved over the last years: from translator to a versatile language professional. The main purpose of our language professionals is to provide multilingual products and to communicate clearly with multiple audiences in order to foster a better understanding of the work of the European Parliament and its Members. Increasingly, the focus is on three formats: text, audio and video. DG TRAD continues to excel as a global leader in the translation field, thanks to the work of our language professionals and innovative technology