@inproceedings{verges-boncompte-r-costa-jussa-2020-multilingual,
title = "Multilingual Neural Machine Translation: Case-study for {C}atalan, {S}panish and {P}ortuguese {R}omance Languages",
author = "Verg{\'e}s Boncompte, Pere and
R. Costa-juss{\`a}, Marta",
booktitle = "Proceedings of the Fifth Conference on Machine Translation",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.wmt-1.54",
pages = "447--450",
abstract = "In this paper, we describe the TALP-UPC participation in the WMT Similar Language Translation task between Catalan, Spanish, and Portuguese, all of them, Romance languages. We made use of different techniques to improve the translation between these languages. The multilingual shared encoder/decoder has been used for all of them. Additionally, we applied back-translation to take advantage of the monolingual data. Finally, we have applied fine-tuning to improve the in-domain data. Each of these techniques brings improvements over the previous one. In the official evaluation, our system was ranked 1st in the Portuguese-to-Spanish direction, 2nd in the opposite direction, and 3rd in the Catalan-Spanish pair.",
}
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%0 Conference Proceedings
%T Multilingual Neural Machine Translation: Case-study for Catalan, Spanish and Portuguese Romance Languages
%A Vergés Boncompte, Pere
%A R. Costa-jussà, Marta
%S Proceedings of the Fifth Conference on Machine Translation
%D 2020
%8 nov
%I Association for Computational Linguistics
%C Online
%F verges-boncompte-r-costa-jussa-2020-multilingual
%X In this paper, we describe the TALP-UPC participation in the WMT Similar Language Translation task between Catalan, Spanish, and Portuguese, all of them, Romance languages. We made use of different techniques to improve the translation between these languages. The multilingual shared encoder/decoder has been used for all of them. Additionally, we applied back-translation to take advantage of the monolingual data. Finally, we have applied fine-tuning to improve the in-domain data. Each of these techniques brings improvements over the previous one. In the official evaluation, our system was ranked 1st in the Portuguese-to-Spanish direction, 2nd in the opposite direction, and 3rd in the Catalan-Spanish pair.
%U https://aclanthology.org/2020.wmt-1.54
%P 447-450
Markdown (Informal)
[Multilingual Neural Machine Translation: Case-study for Catalan, Spanish and Portuguese Romance Languages](https://aclanthology.org/2020.wmt-1.54) (Vergés Boncompte & R. Costa-jussà, WMT 2020)
ACL