Neural Machine Translation with Source Dependency Representation
Kehai Chen, Rui Wang, Masao Utiyama, Lemao Liu, Akihiro Tamura, Eiichiro Sumita, Tiejun Zhao
Abstract
Source dependency information has been successfully introduced into statistical machine translation. However, there are only a few preliminary attempts for Neural Machine Translation (NMT), such as concatenating representations of source word and its dependency label together. In this paper, we propose a novel NMT with source dependency representation to improve translation performance of NMT, especially long sentences. Empirical results on NIST Chinese-to-English translation task show that our method achieves 1.6 BLEU improvements on average over a strong NMT system.- Anthology ID:
- D17-1304
- Volume:
- Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
- Month:
- September
- Year:
- 2017
- Address:
- Copenhagen, Denmark
- Venue:
- EMNLP
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 2846–2852
- Language:
- URL:
- https://aclanthology.org/D17-1304
- DOI:
- 10.18653/v1/D17-1304
- Cite (ACL):
- Kehai Chen, Rui Wang, Masao Utiyama, Lemao Liu, Akihiro Tamura, Eiichiro Sumita, and Tiejun Zhao. 2017. Neural Machine Translation with Source Dependency Representation. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 2846–2852, Copenhagen, Denmark. Association for Computational Linguistics.
- Cite (Informal):
- Neural Machine Translation with Source Dependency Representation (Chen et al., EMNLP 2017)
- PDF:
- https://preview.aclanthology.org/nodalida-main-page/D17-1304.pdf