Instance Weighting for Neural Machine Translation Domain Adaptation

Rui Wang, Masao Utiyama, Lemao Liu, Kehai Chen, Eiichiro Sumita


Abstract
Instance weighting has been widely applied to phrase-based machine translation domain adaptation. However, it is challenging to be applied to Neural Machine Translation (NMT) directly, because NMT is not a linear model. In this paper, two instance weighting technologies, i.e., sentence weighting and domain weighting with a dynamic weight learning strategy, are proposed for NMT domain adaptation. Empirical results on the IWSLT English-German/French tasks show that the proposed methods can substantially improve NMT performance by up to 2.7-6.7 BLEU points, outperforming the existing baselines by up to 1.6-3.6 BLEU points.
Anthology ID:
D17-1155
Volume:
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
Month:
September
Year:
2017
Address:
Copenhagen, Denmark
Venue:
EMNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
1482–1488
Language:
URL:
https://aclanthology.org/D17-1155
DOI:
10.18653/v1/D17-1155
Bibkey:
Cite (ACL):
Rui Wang, Masao Utiyama, Lemao Liu, Kehai Chen, and Eiichiro Sumita. 2017. Instance Weighting for Neural Machine Translation Domain Adaptation. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 1482–1488, Copenhagen, Denmark. Association for Computational Linguistics.
Cite (Informal):
Instance Weighting for Neural Machine Translation Domain Adaptation (Wang et al., EMNLP 2017)
Copy Citation:
PDF:
https://aclanthology.org/D17-1155.pdf
Code
 wangruinlp/nmt_instance_weighting