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:
- 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)
- PDF:
- https://preview.aclanthology.org/emnlp22-frontmatter/W18-3931.pdf