Reducing Word Omission Errors in Neural Machine Translation: A Contrastive Learning Approach

Zonghan Yang, Yong Cheng, Yang Liu, Maosong Sun


Abstract
While neural machine translation (NMT) has achieved remarkable success, NMT systems are prone to make word omission errors. In this work, we propose a contrastive learning approach to reducing word omission errors in NMT. The basic idea is to enable the NMT model to assign a higher probability to a ground-truth translation and a lower probability to an erroneous translation, which is automatically constructed from the ground-truth translation by omitting words. We design different types of negative examples depending on the number of omitted words, word frequency, and part of speech. Experiments on Chinese-to-English, German-to-English, and Russian-to-English translation tasks show that our approach is effective in reducing word omission errors and achieves better translation performance than three baseline methods.
Anthology ID:
P19-1623
Volume:
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2019
Address:
Florence, Italy
Editors:
Anna Korhonen, David Traum, Lluís Màrquez
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
6191–6196
Language:
URL:
https://aclanthology.org/P19-1623
DOI:
10.18653/v1/P19-1623
Bibkey:
Cite (ACL):
Zonghan Yang, Yong Cheng, Yang Liu, and Maosong Sun. 2019. Reducing Word Omission Errors in Neural Machine Translation: A Contrastive Learning Approach. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 6191–6196, Florence, Italy. Association for Computational Linguistics.
Cite (Informal):
Reducing Word Omission Errors in Neural Machine Translation: A Contrastive Learning Approach (Yang et al., ACL 2019)
Copy Citation:
PDF:
https://aclanthology.org/P19-1623.pdf