Jonathan Mutal


2020

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COPECO: a Collaborative Post-Editing Corpus in Pedagogical Context
Jonathan Mutal | Pierrette Bouillon | Perrine Schumacher | Johanna Gerlach
Proceedings of 1st Workshop on Post-Editing in Modern-Day Translation

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Ellipsis Translation for a Medical Speech to Speech Translation System
Jonathan Mutal | Johanna Gerlach | Pierrette Bouillon | Hervé Spechbach
Proceedings of the 22nd Annual Conference of the European Association for Machine Translation

In diagnostic interviews, elliptical utterances allow doctors to question patients in a more efficient and economical way. However, literal translation of such incomplete utterances is rarely possible without affecting communication. Previous studies have focused on automatic ellipsis detection and resolution, but only few specifically address the problem of automatic translation of ellipsis. In this work, we evaluate four different approaches to translate ellipsis in medical dialogues in the context of the speech to speech translation system BabelDr. We also investigate the impact of training data, using an under-sampling method and data with elliptical utterances in context. Results show that the best model is able to translate 88% of elliptical utterances.

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Re-design of the Machine Translation Training Tool (MT3)
Paula Estrella | Emiliano Cuenca | Laura Bruno | Jonathan Mutal | Sabrina Girletti | Lise Volkart | Pierrette Bouillon
Proceedings of the 22nd Annual Conference of the European Association for Machine Translation

We believe that machine translation (MT) must be introduced to translation students as part of their training, in preparation for their professional life. In this paper we present a new version of the tool called MT3, which builds on and extends a joint effort undertaken by the Faculty of Languages of the University of Córdoba and Faculty of Translation and Interpreting of the University of Geneva to develop an open-source web platform to teach MT to translation students. We also report on a pilot experiment with the goal of testing the viability of using MT3 in an MT course. The pilot let us identify areas for improvement and collect students’ feedback about the tool’s usability.

2019

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Monolingual backtranslation in a medical speech translation system for diagnostic interviews - a NMT approach
Jonathan Mutal | Pierrette Bouillon | Johanna Gerlach | Paula Estrella | Hervé Spechbach
Proceedings of Machine Translation Summit XVII Volume 2: Translator, Project and User Tracks

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Differences between SMT and NMT Output - a Translators’ Point of View
Jonathan Mutal | Lise Volkart | Pierrette Bouillon | Sabrina Girletti | Paula Estrella
Proceedings of the Human-Informed Translation and Interpreting Technology Workshop (HiT-IT 2019)

In this study, we compare the output quality of two MT systems, a statistical (SMT) and a neural (NMT) engine, customised for Swiss Post’s Language Service using the same training data. We focus on the point of view of professional translators and investigate how they perceive the differences between the MT output and a human reference (namely deletions, substitutions, insertions and word order). Our findings show that translators more frequently consider these differences to be errors in SMT than NMT, and that deletions are the most serious errors in both architectures. We also observe lower agreement on differences to be corrected in NMT than in SMT, suggesting that errors are easier to identify in SMT. These findings confirm the ability of NMT to produce correct paraphrases, which could also explain why BLEU is often considered as an inadequate metric to evaluate the performance of NMT systems.