Modeling Localness for Self-Attention Networks
Baosong Yang, Zhaopeng Tu, Derek F. Wong, Fandong Meng, Lidia S. Chao, Tong Zhang
Abstract
Self-attention networks have proven to be of profound value for its strength of capturing global dependencies. In this work, we propose to model localness for self-attention networks, which enhances the ability of capturing useful local context. We cast localness modeling as a learnable Gaussian bias, which indicates the central and scope of the local region to be paid more attention. The bias is then incorporated into the original attention distribution to form a revised distribution. To maintain the strength of capturing long distance dependencies while enhance the ability of capturing short-range dependencies, we only apply localness modeling to lower layers of self-attention networks. Quantitative and qualitative analyses on Chinese-English and English-German translation tasks demonstrate the effectiveness and universality of the proposed approach.- Anthology ID:
- D18-1475
- Volume:
- Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
- Month:
- October-November
- Year:
- 2018
- Address:
- Brussels, Belgium
- Editors:
- Ellen Riloff, David Chiang, Julia Hockenmaier, Jun’ichi Tsujii
- Venue:
- EMNLP
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 4449–4458
- Language:
- URL:
- https://preview.aclanthology.org/add_missing_videos/D18-1475/
- DOI:
- 10.18653/v1/D18-1475
- Cite (ACL):
- Baosong Yang, Zhaopeng Tu, Derek F. Wong, Fandong Meng, Lidia S. Chao, and Tong Zhang. 2018. Modeling Localness for Self-Attention Networks. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 4449–4458, Brussels, Belgium. Association for Computational Linguistics.
- Cite (Informal):
- Modeling Localness for Self-Attention Networks (Yang et al., EMNLP 2018)
- PDF:
- https://preview.aclanthology.org/add_missing_videos/D18-1475.pdf
- Data
- WMT 2014