Abstract
Cross-attention is an important component of neural machine translation (NMT), which is always realized by dot-product attention in previous methods. However, dot-product attention only considers the pair-wise correlation between words, resulting in dispersion when dealing with long sentences and neglect of source neighboring relationships. Inspired by linguistics, the above issues are caused by ignoring a type of cross-attention, called concentrated attention, which focuses on several central words and then spreads around them. In this work, we apply Gaussian Mixture Model (GMM) to model the concentrated attention in cross-attention. Experiments and analyses we conducted on three datasets show that the proposed method outperforms the baseline and has significant improvement on alignment quality, N-gram accuracy, and long sentence translation.- Anthology ID:
- 2021.findings-emnlp.121
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2021
- Month:
- November
- Year:
- 2021
- Address:
- Punta Cana, Dominican Republic
- Editors:
- Marie-Francine Moens, Xuanjing Huang, Lucia Specia, Scott Wen-tau Yih
- Venue:
- Findings
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1401–1411
- Language:
- URL:
- https://aclanthology.org/2021.findings-emnlp.121
- DOI:
- 10.18653/v1/2021.findings-emnlp.121
- Cite (ACL):
- Shaolei Zhang and Yang Feng. 2021. Modeling Concentrated Cross-Attention for Neural Machine Translation with Gaussian Mixture Model. In Findings of the Association for Computational Linguistics: EMNLP 2021, pages 1401–1411, Punta Cana, Dominican Republic. Association for Computational Linguistics.
- Cite (Informal):
- Modeling Concentrated Cross-Attention for Neural Machine Translation with Gaussian Mixture Model (Zhang & Feng, Findings 2021)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-3/2021.findings-emnlp.121.pdf