Abstract
In domain adaptation for neural machine translation, translation performance can benefit from separating features into domain-specific features and common features. In this paper, we propose a method to explicitly model the two kinds of information in the encoder-decoder framework so as to exploit out-of-domain data in in-domain training. In our method, we maintain a private encoder and a private decoder for each domain which are used to model domain-specific information. In the meantime, we introduce a common encoder and a common decoder shared by all the domains which can only have domain-independent information flow through. Besides, we add a discriminator to the shared encoder and employ adversarial training for the whole model to reinforce the performance of information separation and machine translation simultaneously. Experiment results show that our method can outperform competitive baselines greatly on multiple data sets.- Anthology ID:
- N19-1312
- Volume:
- Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
- Month:
- June
- Year:
- 2019
- Address:
- Minneapolis, Minnesota
- Editors:
- Jill Burstein, Christy Doran, Thamar Solorio
- Venue:
- NAACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 3081–3091
- Language:
- URL:
- https://aclanthology.org/N19-1312
- DOI:
- 10.18653/v1/N19-1312
- Cite (ACL):
- Shuhao Gu, Yang Feng, and Qun Liu. 2019. Improving Domain Adaptation Translation with Domain Invariant and Specific Information. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 3081–3091, Minneapolis, Minnesota. Association for Computational Linguistics.
- Cite (Informal):
- Improving Domain Adaptation Translation with Domain Invariant and Specific Information (Gu et al., NAACL 2019)
- PDF:
- https://preview.aclanthology.org/naacl24-info/N19-1312.pdf