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A few clicks and anyone can convince themselves that machine translation (MT) software now performs well enough to offer a bona fide service, from which professional translators and lay users alike may benefit.
The users and uses of MT tools are many: primarily used to view material written in a language that the user does not know, it can also be used to provide information in other languages, communicate with speakers of another language, increase the production speed of human translations, help with writing in a foreign language, learn new languages, and so on. Some MT uses do not even involve any human reading or writing. For example, MT can be used to consistently index or categorize documents written in several languages for the purposes of information searching or detecting fake or hateful content. The material that is put through MT systems is no less varied, ranging from single words or terms to technical and literary texts, as well as all forms of content available online: websites, forum posts, tweets, and so on. It is therefore unsurprising that online translation services are being used on an ever-increasing scale, with billions of translation requests covering several thousand language pairs being served every day.
This improvement in MT is the result of relatively recent technological innovations in the field of language processing and stems from the convergence of several factors. First and foremost is the development of computational models, which were once based on probabilistic models and today use “neural” computing architectures…


Machine translation has made sudden and spectacular progress of late, and computers are (finally) capable of producing translations that are useful for a wide range of situations. Should translators be concerned about this new form of competition? In this article, a study of “translationese,” the language of translation, explains why fully automated machine translation remains a very distant goal. In the short term, however, advances in the field are enabling humans and machines to work together more effectively.

  • machine translation
  • neural translation
  • computer-aided translation
  • language of translation
François Yvon
François Yvon is a researcher at the Centre national de la recherche scientifique (CNRS) (French National Centre for Scientific Research). He conducts his research within the “Traitement du langage parlé” (natural language processing) group at LIMSI (Laboratoire d’informatique pour la mécanique et les sciences de l’ingénieur) (Computer Science Laboratory for Mechanics and Engineering Sciences) in Orsay, a laboratory that he has been running since 2013. His recent work focuses mainly on machine translation using statistical and neural methods, and more generally on machine learning applied to multilingual language data, whether written or oral. On this theme, he leads the activities of a collective composed of about ten researchers, doctoral students, and post-doctoral fellows. Yvon has overseen numerous national and international projects, and is a member of the executive board of the European Meta-NET network. Previously, he taught computer science at Université Paris-Sud and Télécom Paris. He has written around thirty articles published in scientific journals and approximately two hundred papers for international conferences. He has also supervised or co-supervised around twenty doctoral students.
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Uploaded on on 29/07/2020
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