Towards Robust Neural Machine Translation
Yong Cheng, Zhaopeng Tu, Fandong Meng, Junjie Zhai, Yang Liu
Abstract
Small perturbations in the input can severely distort intermediate representations and thus impact translation quality of neural machine translation (NMT) models. In this paper, we propose to improve the robustness of NMT models with adversarial stability training. The basic idea is to make both the encoder and decoder in NMT models robust against input perturbations by enabling them to behave similarly for the original input and its perturbed counterpart. Experimental results on Chinese-English, English-German and English-French translation tasks show that our approaches can not only achieve significant improvements over strong NMT systems but also improve the robustness of NMT models.- Anthology ID:
- P18-1163
- Volume:
- Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Month:
- July
- Year:
- 2018
- Address:
- Melbourne, Australia
- Editors:
- Iryna Gurevych, Yusuke Miyao
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1756–1766
- Language:
- URL:
- https://aclanthology.org/P18-1163
- DOI:
- 10.18653/v1/P18-1163
- Cite (ACL):
- Yong Cheng, Zhaopeng Tu, Fandong Meng, Junjie Zhai, and Yang Liu. 2018. Towards Robust Neural Machine Translation. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1756–1766, Melbourne, Australia. Association for Computational Linguistics.
- Cite (Informal):
- Towards Robust Neural Machine Translation (Cheng et al., ACL 2018)
- PDF:
- https://preview.aclanthology.org/ml4al-ingestion/P18-1163.pdf