Jonathan Mutal


Producing Standard German Subtitles for Swiss German TV Content
Johanna Gerlach | Jonathan Mutal | Bouillon Pierrette
Ninth Workshop on Speech and Language Processing for Assistive Technologies (SLPAT-2022)

In this study we compare two approaches (neural machine translation and edit-based) and the use of synthetic data for the task of translating normalised Swiss German ASR output into correct written Standard German for subtitles, with a special focus on syntactic differences. Results suggest that NMT is better suited to this task and that relatively simple rule-based generation of training data could be a valuable approach for cases where little training data is available and transformations are simple.

A Neural Machine Translation Approach to Translate Text to Pictographs in a Medical Speech Translation System - The BabelDr Use Case
Jonathan Mutal | Pierrette Bouillon | Magali Norré | Johanna Gerlach | Lucia Ormaechea Grijalba
Proceedings of the 15th biennial conference of the Association for Machine Translation in the Americas (Volume 1: Research Track)

The use of images has been shown to positively affect patient comprehension in medical settings, in particular to deliver specific medical instructions. However, tools that automatically translate sentences into pictographs are still scarce due to the lack of resources. Previous studies have focused on the translation of sentences into pictographs by using WordNet combined with rule-based approaches and deep learning methods. In this work, we showed how we leveraged the BabelDr system, a speech to speech translator for medical triage, to build a speech to pictograph translator using UMLS and neural machine translation approaches. We showed that the translation from French sentences to a UMLS gloss can be viewed as a machine translation task and that a Multilingual Neural Machine Translation system achieved the best results.

The PASSAGE project : Standard German Subtitling of Swiss German TV content
Pierrette Bouillon | Johanna Gerlach | Jonathan Mutal | Marianne Starlander
Proceedings of the 23rd Annual Conference of the European Association for Machine Translation

We present the PASSAGE project, which aims at automatic Standard German subtitling of Swiss German TV content. This is achieved in a two step process, beginning with ASR to produce a normalised transcription, followed by translation into Standard German. We focus on the second step, for which we explore different approaches and contribute aligned corpora for future research.


A Speech-enabled Fixed-phrase Translator for Healthcare Accessibility
Pierrette Bouillon | Johanna Gerlach | Jonathan Mutal | Nikos Tsourakis | Hervé Spechbach
Proceedings of the 1st Workshop on NLP for Positive Impact

In this overview article we describe an application designed to enable communication between health practitioners and patients who do not share a common language, in situations where professional interpreters are not available. Built on the principle of a fixed phrase translator, the application implements different natural language processing (NLP) technologies, such as speech recognition, neural machine translation and text-to-speech to improve usability. Its design allows easy portability to new domains and integration of different types of output for multiple target audiences. Even though BabelDr is far from solving the problem of miscommunication between patients and doctors, it is a clear example of NLP in a real world application designed to help minority groups to communicate in a medical context. It also gives some insights into the relevant criteria for the development of such an application.


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

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.

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.


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: Translator, Project and User Tracks

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.