Using Word Vectors to Improve Word Alignments for Low Resource Machine Translation

Nima Pourdamghani, Marjan Ghazvininejad, Kevin Knight


Abstract
We present a method for improving word alignments using word similarities. This method is based on encouraging common alignment links between semantically similar words. We use word vectors trained on monolingual data to estimate similarity. Our experiments on translating fifteen languages into English show consistent BLEU score improvements across the languages.
Anthology ID:
N18-2083
Volume:
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)
Month:
June
Year:
2018
Address:
New Orleans, Louisiana
Editors:
Marilyn Walker, Heng Ji, Amanda Stent
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
524–528
Language:
URL:
https://aclanthology.org/N18-2083
DOI:
10.18653/v1/N18-2083
Bibkey:
Cite (ACL):
Nima Pourdamghani, Marjan Ghazvininejad, and Kevin Knight. 2018. Using Word Vectors to Improve Word Alignments for Low Resource Machine Translation. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers), pages 524–528, New Orleans, Louisiana. Association for Computational Linguistics.
Cite (Informal):
Using Word Vectors to Improve Word Alignments for Low Resource Machine Translation (Pourdamghani et al., NAACL 2018)
Copy Citation:
PDF:
https://aclanthology.org/N18-2083.pdf