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
- 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)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/N18-2083.pdf