Neural Machine Translation with Recurrent Attention Modeling
Zichao Yang, Zhiting Hu, Yuntian Deng, Chris Dyer, Alex Smola
Abstract
Knowing which words have been attended to in previous time steps while generating a translation is a rich source of information for predicting what words will be attended to in the future. We improve upon the attention model of Bahdanau et al. (2014) by explicitly modeling the relationship between previous and subsequent attention levels for each word using one recurrent network per input word. This architecture easily captures informative features, such as fertility and regularities in relative distortion. In experiments, we show our parameterization of attention improves translation quality.- Anthology ID:
- E17-2061
- Volume:
- Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers
- Month:
- April
- Year:
- 2017
- Address:
- Valencia, Spain
- Editors:
- Mirella Lapata, Phil Blunsom, Alexander Koller
- Venue:
- EACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 383–387
- Language:
- URL:
- https://aclanthology.org/E17-2061
- DOI:
- Cite (ACL):
- Zichao Yang, Zhiting Hu, Yuntian Deng, Chris Dyer, and Alex Smola. 2017. Neural Machine Translation with Recurrent Attention Modeling. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers, pages 383–387, Valencia, Spain. Association for Computational Linguistics.
- Cite (Informal):
- Neural Machine Translation with Recurrent Attention Modeling (Yang et al., EACL 2017)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/E17-2061.pdf