A Neural Approach to Language Variety Translation

Marta R. Costa-jussà, Marcos Zampieri, Santanu Pal


Abstract
In this paper we present the first neural-based machine translation system trained to translate between standard national varieties of the same language. We take the pair Brazilian - European Portuguese as an example and compare the performance of this method to a phrase-based statistical machine translation system. We report a performance improvement of 0.9 BLEU points in translating from European to Brazilian Portuguese and 0.2 BLEU points when translating in the opposite direction. We also carried out a human evaluation experiment with native speakers of Brazilian Portuguese which indicates that humans prefer the output produced by the neural-based system in comparison to the statistical system.
Anthology ID:
W18-3931
Volume:
Proceedings of the Fifth Workshop on NLP for Similar Languages, Varieties and Dialects (VarDial 2018)
Month:
August
Year:
2018
Address:
Santa Fe, New Mexico, USA
Editors:
Marcos Zampieri, Preslav Nakov, Nikola Ljubešić, Jörg Tiedemann, Shervin Malmasi, Ahmed Ali
Venue:
VarDial
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
275–282
Language:
URL:
https://aclanthology.org/W18-3931
DOI:
Bibkey:
Cite (ACL):
Marta R. Costa-jussà, Marcos Zampieri, and Santanu Pal. 2018. A Neural Approach to Language Variety Translation. In Proceedings of the Fifth Workshop on NLP for Similar Languages, Varieties and Dialects (VarDial 2018), pages 275–282, Santa Fe, New Mexico, USA. Association for Computational Linguistics.
Cite (Informal):
A Neural Approach to Language Variety Translation (Costa-jussà et al., VarDial 2018)
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PDF:
https://aclanthology.org/W18-3931.pdf