Curriculum Learning for Domain Adaptation in Neural Machine Translation

Xuan Zhang, Pamela Shapiro, Gaurav Kumar, Paul McNamee, Marine Carpuat, Kevin Duh


Abstract
We introduce a curriculum learning approach to adapt generic neural machine translation models to a specific domain. Samples are grouped by their similarities to the domain of interest and each group is fed to the training algorithm with a particular schedule. This approach is simple to implement on top of any neural framework or architecture, and consistently outperforms both unadapted and adapted baselines in experiments with two distinct domains and two language pairs.
Anthology ID:
N19-1189
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
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1903–1915
Language:
URL:
https://aclanthology.org/N19-1189
DOI:
10.18653/v1/N19-1189
Bibkey:
Cite (ACL):
Xuan Zhang, Pamela Shapiro, Gaurav Kumar, Paul McNamee, Marine Carpuat, and Kevin Duh. 2019. Curriculum Learning for Domain Adaptation in Neural Machine Translation. 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 1903–1915, Minneapolis, Minnesota. Association for Computational Linguistics.
Cite (Informal):
Curriculum Learning for Domain Adaptation in Neural Machine Translation (Zhang et al., NAACL 2019)
Copy Citation:
PDF:
https://aclanthology.org/N19-1189.pdf