InDeep × NMT: Empowering Human Translators via Interpretable Neural Machine Translation

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Neural machine translation (NMT) systems are nowadays essential components of professional translation workflows. Consequently, human translators are increasingly working as post-editors for machine-translated content. The NWO-funded InDeep project aims to empower users of Deep Learning models of text, speech, and music by improving their ability to interact with such models and interpret their behaviors. In the specific context of translation, we aim at developing new tools and methodologies to improve prediction attribution, error analysis, and controllable generation for NMT systems. These advances will be evaluated through field studies involving professional translators to assess gains in efficiency and overall enjoyability of the post-editing process.
Original languageEnglish
Title of host publicationProceedings of the 23rd Annual Conference of the European Association for Machine Translation
EditorsHelena Moniz, Lieve Macken, Andrew Rufener, Loïc Barrault, Marta R. Costa-jussà, Christophe Declercq, Maarit Koponen, Ellie Kemp, Spyridon Pilos, Mikel L. Forcada, Carolina Scarton, Joachim van den Bogaert, Joke Daems, Arda Tezcan, Bram Vanroy, Margot Fonteyne
PublisherEuropean Association for Machine Translation
Number of pages2
Publication statusPublished - Jun-2022
Event23rd Annual Conference of the European Association for Machine Translation - Zebrastraat, Ghent, Belgium
Duration: 1-Jun-20223-Jun-2022


Conference23rd Annual Conference of the European Association for Machine Translation
Abbreviated titleEAMT '22
Internet address


  • machine translation
  • interpretability

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