Improved Neural Machine Translation with a Syntax-Aware Encoder and Decoder

Huadong Chen, Shujian Huang, David Chiang, Jiajun Chen


Abstract
Most neural machine translation (NMT) models are based on the sequential encoder-decoder framework, which makes no use of syntactic information. In this paper, we improve this model by explicitly incorporating source-side syntactic trees. More specifically, we propose (1) a bidirectional tree encoder which learns both sequential and tree structured representations; (2) a tree-coverage model that lets the attention depend on the source-side syntax. Experiments on Chinese-English translation demonstrate that our proposed models outperform the sequential attentional model as well as a stronger baseline with a bottom-up tree encoder and word coverage.
Anthology ID:
P17-1177
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:
1936–1945
Language:
URL:
https://aclanthology.org/P17-1177
DOI:
10.18653/v1/P17-1177
Bibkey:
Cite (ACL):
Huadong Chen, Shujian Huang, David Chiang, and Jiajun Chen. 2017. Improved Neural Machine Translation with a Syntax-Aware Encoder and Decoder. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1936–1945, Vancouver, Canada. Association for Computational Linguistics.
Cite (Informal):
Improved Neural Machine Translation with a Syntax-Aware Encoder and Decoder (Chen et al., ACL 2017)
Copy Citation:
PDF:
https://aclanthology.org/P17-1177.pdf
Code
 howardchenhd/Syntax-awared-NMT