Towards Bidirectional Hierarchical Representations for Attention-based Neural Machine Translation
Baosong Yang, Derek F. Wong, Tong Xiao, Lidia S. Chao, Jingbo Zhu
Abstract
This paper proposes a hierarchical attentional neural translation model which focuses on enhancing source-side hierarchical representations by covering both local and global semantic information using a bidirectional tree-based encoder. To maximize the predictive likelihood of target words, a weighted variant of an attention mechanism is used to balance the attentive information between lexical and phrase vectors. Using a tree-based rare word encoding, the proposed model is extended to sub-word level to alleviate the out-of-vocabulary (OOV) problem. Empirical results reveal that the proposed model significantly outperforms sequence-to-sequence attention-based and tree-based neural translation models in English-Chinese translation tasks.- Anthology ID:
- D17-1150
- Volume:
- Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
- Month:
- September
- Year:
- 2017
- Address:
- Copenhagen, Denmark
- Editors:
- Martha Palmer, Rebecca Hwa, Sebastian Riedel
- Venue:
- EMNLP
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1432–1441
- Language:
- URL:
- https://aclanthology.org/D17-1150
- DOI:
- 10.18653/v1/D17-1150
- Cite (ACL):
- Baosong Yang, Derek F. Wong, Tong Xiao, Lidia S. Chao, and Jingbo Zhu. 2017. Towards Bidirectional Hierarchical Representations for Attention-based Neural Machine Translation. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 1432–1441, Copenhagen, Denmark. Association for Computational Linguistics.
- Cite (Informal):
- Towards Bidirectional Hierarchical Representations for Attention-based Neural Machine Translation (Yang et al., EMNLP 2017)
- PDF:
- https://preview.aclanthology.org/teach-a-man-to-fish/D17-1150.pdf