Active Learning Approaches to Enhancing Neural Machine Translation

Yuekai Zhao, Haoran Zhang, Shuchang Zhou, Zhihua Zhang


Abstract
Active learning is an efficient approach for mitigating data dependency when training neural machine translation (NMT) models. In this paper, we explore new training frameworks by incorporating active learning into various techniques such as transfer learning and iterative back-translation (IBT) under a limited human translation budget. We design a word frequency based acquisition function and combine it with a strong uncertainty based method. The combined method steadily outperforms all other acquisition functions in various scenarios. As far as we know, we are the first to do a large-scale study on actively training Transformer for NMT. Specifically, with a human translation budget of only 20% of the original parallel corpus, we manage to surpass Transformer trained on the entire parallel corpus in three language pairs.
Anthology ID:
2020.findings-emnlp.162
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2020
Month:
November
Year:
2020
Address:
Online
Venues:
EMNLP | Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1796–1806
Language:
URL:
https://aclanthology.org/2020.findings-emnlp.162
DOI:
10.18653/v1/2020.findings-emnlp.162
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/2020.findings-emnlp.162.pdf
Optional supplementary material:
 2020.findings-emnlp.162.OptionalSupplementaryMaterial.pdf