When do Contrastive Word Alignments Improve Many-to-many Neural Machine Translation?
Zhuoyuan Mao, Chenhui Chu, Raj Dabre, Haiyue Song, Zhen Wan, Sadao Kurohashi
Abstract
Word alignment has proven to benefit many-to-many neural machine translation (NMT). However, high-quality ground-truth bilingual dictionaries were used for pre-editing in previous methods, which are unavailable for most language pairs. Meanwhile, the contrastive objective can implicitly utilize automatically learned word alignment, which has not been explored in many-to-many NMT. This work proposes a word-level contrastive objective to leverage word alignments for many-to-many NMT. Empirical results show that this leads to 0.8 BLEU gains for several language pairs. Analyses reveal that in many-to-many NMT, the encoder’s sentence retrieval performance highly correlates with the translation quality, which explains when the proposed method impacts translation. This motivates future exploration for many-to-many NMT to improve the encoder’s sentence retrieval performance.- Anthology ID:
- 2022.findings-naacl.134
- Volume:
- Findings of the Association for Computational Linguistics: NAACL 2022
- Month:
- July
- Year:
- 2022
- Address:
- Seattle, United States
- Editors:
- Marine Carpuat, Marie-Catherine de Marneffe, Ivan Vladimir Meza Ruiz
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1766–1775
- Language:
- URL:
- https://aclanthology.org/2022.findings-naacl.134
- DOI:
- 10.18653/v1/2022.findings-naacl.134
- Cite (ACL):
- Zhuoyuan Mao, Chenhui Chu, Raj Dabre, Haiyue Song, Zhen Wan, and Sadao Kurohashi. 2022. When do Contrastive Word Alignments Improve Many-to-many Neural Machine Translation?. In Findings of the Association for Computational Linguistics: NAACL 2022, pages 1766–1775, Seattle, United States. Association for Computational Linguistics.
- Cite (Informal):
- When do Contrastive Word Alignments Improve Many-to-many Neural Machine Translation? (Mao et al., Findings 2022)
- PDF:
- https://preview.aclanthology.org/naacl24-info/2022.findings-naacl.134.pdf
- Data
- word2word