Bridging the Data Gap between Training and Inference for Unsupervised Neural Machine Translation

Zhiwei He, Xing Wang, Rui Wang, Shuming Shi, Zhaopeng Tu


Abstract
Back-translation is a critical component of Unsupervised Neural Machine Translation (UNMT), which generates pseudo parallel data from target monolingual data. A UNMT model is trained on the pseudo parallel data with \bf translated source, and translates \bf natural source sentences in inference. The source discrepancy between training and inference hinders the translation performance of UNMT models. By carefully designing experiments, we identify two representative characteristics of the data gap in source: (1) style gap (i.e., translated vs. natural text style) that leads to poor generalization capability; (2) content gap that induces the model to produce hallucination content biased towards the target language. To narrow the data gap, we propose an online self-training approach, which simultaneously uses the pseudo parallel data {natural source, translated target} to mimic the inference scenario. Experimental results on several widely-used language pairs show that our approach outperforms two strong baselines (XLM and MASS) by remedying the style and content gaps.
Anthology ID:
2022.acl-long.456
Volume:
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
May
Year:
2022
Address:
Dublin, Ireland
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
6611–6623
Language:
URL:
https://aclanthology.org/2022.acl-long.456
DOI:
10.18653/v1/2022.acl-long.456
Bibkey:
Cite (ACL):
Zhiwei He, Xing Wang, Rui Wang, Shuming Shi, and Zhaopeng Tu. 2022. Bridging the Data Gap between Training and Inference for Unsupervised Neural Machine Translation. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 6611–6623, Dublin, Ireland. Association for Computational Linguistics.
Cite (Informal):
Bridging the Data Gap between Training and Inference for Unsupervised Neural Machine Translation (He et al., ACL 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.acl-long.456.pdf
Software:
 2022.acl-long.456.software.zip
Code
 zwhe99/selftraining4unmt