Abstract
This paper proposes three distortion models to explicitly incorporate the word reordering knowledge into attention-based Neural Machine Translation (NMT) for further improving translation performance. Our proposed models enable attention mechanism to attend to source words regarding both the semantic requirement and the word reordering penalty. Experiments on Chinese-English translation show that the approaches can improve word alignment quality and achieve significant translation improvements over a basic attention-based NMT by large margins. Compared with previous works on identical corpora, our system achieves the state-of-the-art performance on translation quality.- Anthology ID:
- P17-1140
- Volume:
- Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Month:
- July
- Year:
- 2017
- Address:
- Vancouver, Canada
- Editors:
- Regina Barzilay, Min-Yen Kan
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1524–1534
- Language:
- URL:
- https://aclanthology.org/P17-1140
- DOI:
- 10.18653/v1/P17-1140
- Cite (ACL):
- Jinchao Zhang, Mingxuan Wang, Qun Liu, and Jie Zhou. 2017. Incorporating Word Reordering Knowledge into Attention-based Neural Machine Translation. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1524–1534, Vancouver, Canada. Association for Computational Linguistics.
- Cite (Informal):
- Incorporating Word Reordering Knowledge into Attention-based Neural Machine Translation (Zhang et al., ACL 2017)
- PDF:
- https://preview.aclanthology.org/ingest-2024-clasp/P17-1140.pdf