Pilar Sánchez-Gijón


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A social media NMT engine for a low-resource language combination
María Do Campo Bayón | Pilar Sánchez-Gijón
Proceedings of the 24th Annual Conference of the European Association for Machine Translation

The aim of this article is to present a new Neural Machine Translation (NMT) from Spanish into Galician for the social media domain that was trained with a Twitter corpus. Our main goal is to outline the methods used to build the corpus and the steps taken to train the engine in a low-resource language context. We have evalu-ated the engine performance both with regular automatic metrics and with a new methodology based on the non-inferiority process and contrasted this information with an error classification human evalua-tion conducted by professional linguists. We will present the steps carried out fol-lowing the conclusions of a previous pilot study, describe the new process followed, analyze the new engine and present the final conclusions.


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MultitraiNMT Erasmus+ project: Machine Translation Training for multilingual citizens (multitrainmt.eu)
Mikel L. Forcada | Pilar Sánchez-Gijón | Dorothy Kenny | Felipe Sánchez-Martínez | Juan Antonio Pérez Ortiz | Riccardo Superbo | Gema Ramírez Sánchez | Olga Torres-Hostench | Caroline Rossi
Proceedings of the 23rd Annual Conference of the European Association for Machine Translation

The MultitraiNMT Erasmus+ project has developed an open innovative syl-labus in machine translation, focusing on neural machine translation (NMT) and targeting both language learners and translators. The training materials include an open access coursebook with more than 250 activities and a pedagogical NMT interface called MutNMT that allows users to learn how neural machine translation works. These materials will allow students to develop the technical and ethical skills and competences required to become informed, critical users of machine translation in their own language learn-ing and translation practice. The pro-ject started in July 2019 and it will end in July 2022.


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MultiTraiNMT: Training Materials to Approach Neural Machine Translation from Scratch
Gema Ramírez-Sánchez | Juan Antonio Pérez-Ortiz | Felipe Sánchez-Martínez | Caroline Rossi | Dorothy Kenny | Riccardo Superbo | Pilar Sánchez-Gijón | Olga Torres-Hostench
Proceedings of the Translation and Interpreting Technology Online Conference

The MultiTraiNMT Erasmus+ project aims at developing an open innovative syllabus in neural machine translation (NMT) for language learners and translators as multilingual citizens. Machine translation is seen as a resource that can support citizens in their attempt to acquire and develop language skills if they are trained in an informed and critical way. Machine translation could thus help tackle the mismatch between the desired EU aim of having multilingual citizens who speak at least two foreign languages and the current situation in which citizens generally fall far short of this objective. The training materials consists of an open-access coursebook, an open-source NMT web application called MutNMT for training purposes, and corresponding activities.


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Evaluating machine translation in a low-resource language combination: Spanish-Galician.
María Do Campo Bayón | Pilar Sánchez-Gijón
Proceedings of Machine Translation Summit XVII: Translator, Project and User Tracks


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Perception vs. Acceptability of TM and SMT Output: What do translators prefer?
Pilar Sánchez-Gijón | Joss Moorkens | Andy Way
Proceedings of the 21st Annual Conference of the European Association for Machine Translation

This paper reports the results of two studies carried out with two different group of professional translators to find out how professionals perceive and accept SMT in comparison with TM. The first group translated and post-edited segments from English into German, and the second group from English into Spanish. Both studies had equivalent settings in order to guarantee the comparability of the results. It will also help to shed light upon the real benefit of SMT from which translators may take advantage.

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Empowering Translators with MTradumàtica: A Do-It-Yourself statistical machine translation platform
Adrià Martín-Mor | Pilar Sánchez-Gijón
Proceedings of the 21st Annual Conference of the European Association for Machine Translation

According to Torres Hostench et al. (2016), the use of machine translation (MT) in Catalan and Spanish translation companies is low. Based on these results, the Tradumàtica research group,2 through the ProjecTA and ProjecTA-U projects,3 set to bring MT and translators closer with a two-fold strategy. On the one hand, by developing MTradumàtica, a free Moses-based web platform with graphical user interface (GUI) for statistical machine translation (SMT) trainers. On the other hand, by including MT-related contents in translators’ training. This paper will describe the latest developments in MTradumàtica.


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The use of machine translation and post-editing among language service providers in Spain
Olga Torres | Ramon Piqué Huerta | Marisa Presas Corbella | Pilar Sánchez-Gijón | Adrià Martín Mor | Pilar Cid-Leal | Anna Aguilar-Amat | Celia Rico-Pérez | Amparo Alcina-Claudet | Miguel Ángel Candel-Mora
Proceedings of Translating and the Computer 37


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MT post-editing into the mother tongue of into a foreign language? Spanish-to-English MT translation output post-edited by translation trainees
Pilar Sánchez-Gijón | Olga Torres-Hostench
Proceedings of the 11th Conference of the Association for Machine Translation in the Americas

The aim of this study is to analyse whether translation trainees who are not native speakers of the target language are able to perform as well as those who are native speakers, and whether they achieve the expected quality in a “good enough” post-editing (PE) job. In particular the study focuses on the performance of two groups of students doing PE from Spanish into English: native English speakers and native Spanish speakers. A pilot study was set up to collect evidence to compare and contrast the two groups’ performances. Trainees from both groups had been given the same training in PE and were asked to post-edit 30 sentences translated from Spanish to English. The PE output was analyzed taking into account accuracy errors (mistranslations and omissions) as well as language errors (grammatical errors and syntax errors). The results show that some native Spanish speakers corrected just as many errors as the native English speakers. Furthermore, the Spanish-speaking trainees outperformed their English-speaking counterparts when identifying mistranslations and omissions. Moreover, the performances of the best English-speaking and Spanish-speaking trainees at identifying grammar and syntax errors were very similar.