Reducing Word Omission Errors in Neural Machine Translation: A Contrastive Learning Approach
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
- 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)
- PDF:
- https://preview.aclanthology.org/teach-a-man-to-fish/P19-1623.pdf