Multi-Domain Neural Machine Translation with Word-Level Domain Context Discrimination
Jiali Zeng, Jinsong Su, Huating Wen, Yang Liu, Jun Xie, Yongjing Yin, Jianqiang Zhao
Abstract
With great practical value, the study of Multi-domain Neural Machine Translation (NMT) mainly focuses on using mixed-domain parallel sentences to construct a unified model that allows translation to switch between different domains. Intuitively, words in a sentence are related to its domain to varying degrees, so that they will exert disparate impacts on the multi-domain NMT modeling. Based on this intuition, in this paper, we devote to distinguishing and exploiting word-level domain contexts for multi-domain NMT. To this end, we jointly model NMT with monolingual attention-based domain classification tasks and improve NMT as follows: 1) Based on the sentence representations produced by a domain classifier and an adversarial domain classifier, we generate two gating vectors and use them to construct domain-specific and domain-shared annotations, for later translation predictions via different attention models; 2) We utilize the attention weights derived from target-side domain classifier to adjust the weights of target words in the training objective, enabling domain-related words to have greater impacts during model training. Experimental results on Chinese-English and English-French multi-domain translation tasks demonstrate the effectiveness of the proposed model. Source codes of this paper are available on Github https://github.com/DeepLearnXMU/WDCNMT.- Anthology ID:
- D18-1041
- 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:
- 447–457
- Language:
- URL:
- https://aclanthology.org/D18-1041
- DOI:
- 10.18653/v1/D18-1041
- Cite (ACL):
- Jiali Zeng, Jinsong Su, Huating Wen, Yang Liu, Jun Xie, Yongjing Yin, and Jianqiang Zhao. 2018. Multi-Domain Neural Machine Translation with Word-Level Domain Context Discrimination. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 447–457, Brussels, Belgium. Association for Computational Linguistics.
- Cite (Informal):
- Multi-Domain Neural Machine Translation with Word-Level Domain Context Discrimination (Zeng et al., EMNLP 2018)
- PDF:
- https://preview.aclanthology.org/teach-a-man-to-fish/D18-1041.pdf
- Code
- DeepLearnXMU/WDCNMT