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
Venues:
COLING | VarDial | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
275–282
Language:
URL:
https://www.aclweb.org/anthology/W18-3931
DOI:
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PDF:
http://aclanthology.lst.uni-saarland.de/W18-3931.pdf