Sabrina Girletti


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Working with Pre-translated Texts: Preliminary Findings from a Survey on Post-editing and Revision Practices in Swiss Corporate In-house Language Services
Sabrina Girletti
Proceedings of the 23rd Annual Conference of the European Association for Machine Translation

With the arrival of neural machine translation, the boundaries between revision and post-editing (PE) have started to blur (Koponen et al., 2020). To shed light on current professional practices and provide new pedagogical perspectives, we set up a survey-based study to investigate how PE and revision are carried out in professional settings. We received 86 responses from corporate translators working at 26 different corporate in-house language services in Switzerland. Although the differences between the two activities seem to be clear for in-house linguists, our findings show that they tend to use the same reading strategies when working with human-translated and machine-translated texts.


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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.


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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.


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Integrating MT at Swiss Post’s Language Service: preliminary results
Pierrette Bouillon | Sabrina Girletti | Paula Estrella | Jonathan Mutal | Martina Bellodi | Beatrice Bircher
Proceedings of the 21st Annual Conference of the European Association for Machine Translation

This paper presents the preliminary results of an ongoing academia-industry collaboration that aims to integrate MT into the workflow of Swiss Post’s Language Service. We describe the evaluations carried out to select an MT tool (commercial or open-source) and assess the suitability of machine translation for post-editing in Swiss Post’s various subject areas and language pairs. The goal of this first phase is to provide recommendations with regard to the tool, language pair and most suitable domain for implementing MT.