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
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
Bibkey:
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)
Copy Citation:
PDF:
https://preview.aclanthology.org/ingestion-script-update/D18-1041.pdf
Code
 DeepLearnXMU/WDCNMT