Towards Robust Neural Machine Translation with Iterative Scheduled Data-Switch Training

Zhongjian Miao, Xiang Li, Liyan Kang, Wen Zhang, Chulun Zhou, Yidong Chen, Bin Wang, Min Zhang, Jinsong Su


Abstract
Most existing methods on robust neural machine translation (NMT) construct adversarial examples by injecting noise into authentic examples and indiscriminately exploit two types of examples. They require the model to translate both the authentic source sentence and its adversarial counterpart into the identical target sentence within the same training stage, which may be a suboptimal choice to achieve robust NMT. In this paper, we first conduct a preliminary study to confirm this claim and further propose an Iterative Scheduled Data-switch Training Framework to mitigate this problem. Specifically, we introduce two training stages, iteratively switching between authentic and adversarial examples. Compared with previous studies, our model focuses more on just one type of examples at each single stage, which can better exploit authentic and adversarial examples, and thus obtaining a better robust NMT model. Moreover, we introduce an improved curriculum learning method with a sampling strategy to better schedule the process of noise injection. Experimental results show that our model significantly surpasses several competitive baselines on four translation benchmarks. Our source code is available at https://github.com/DeepLearnXMU/RobustNMT-ISDST.
Anthology ID:
2022.coling-1.468
Volume:
Proceedings of the 29th International Conference on Computational Linguistics
Month:
October
Year:
2022
Address:
Gyeongju, Republic of Korea
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
5266–5277
Language:
URL:
https://aclanthology.org/2022.coling-1.468
DOI:
Bibkey:
Cite (ACL):
Zhongjian Miao, Xiang Li, Liyan Kang, Wen Zhang, Chulun Zhou, Yidong Chen, Bin Wang, Min Zhang, and Jinsong Su. 2022. Towards Robust Neural Machine Translation with Iterative Scheduled Data-Switch Training. In Proceedings of the 29th International Conference on Computational Linguistics, pages 5266–5277, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
Cite (Informal):
Towards Robust Neural Machine Translation with Iterative Scheduled Data-Switch Training (Miao et al., COLING 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.coling-1.468.pdf
Code
 deeplearnxmu/robustnmt-isdst
Data
MTNT