Improving Zero-shot Multilingual Neural Machine Translation by Leveraging Cross-lingual Consistency Regularization

Pengzhi Gao, Liwen Zhang, Zhongjun He, Hua Wu, Haifeng Wang

Abstract
The multilingual neural machine translation (NMT) model has a promising capability of zero-shot translation, where it could directly translate between language pairs unseen during training. For good transfer performance from supervised directions to zero-shot directions, the multilingual NMT model is expected to learn universal representations across different languages. This paper introduces a cross-lingual consistency regularization, CrossConST, to bridge the representation gap among different languages and boost zero-shot translation performance. The theoretical analysis shows that CrossConST implicitly maximizes the probability distribution for zero-shot translation, and the experimental results on both low-resource and high-resource benchmarks show that CrossConST consistently improves the translation performance. The experimental analysis also proves that CrossConST could close the sentence representation gap and better align the representation space. Given the universality and simplicity of CrossConST, we believe it can serve as a strong baseline for future multilingual NMT research.
Anthology ID:
2023.findings-acl.766
Volume:
Findings of the Association for Computational Linguistics: ACL 2023
Month:
July
Year:
2023
Address:
Toronto, Canada
Editors:
Anna Rogers, Jordan Boyd-Graber, Naoaki Okazaki
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
12103–12119
Language:
URL:
https://aclanthology.org/2023.findings-acl.766
DOI:
10.18653/v1/2023.findings-acl.766
Bibkey:
Cite (ACL):
Pengzhi Gao, Liwen Zhang, Zhongjun He, Hua Wu, and Haifeng Wang. 2023. Improving Zero-shot Multilingual Neural Machine Translation by Leveraging Cross-lingual Consistency Regularization. In Findings of the Association for Computational Linguistics: ACL 2023, pages 12103–12119, Toronto, Canada. Association for Computational Linguistics.
Cite (Informal):
Improving Zero-shot Multilingual Neural Machine Translation by Leveraging Cross-lingual Consistency Regularization (Gao et al., Findings 2023)
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https://preview.aclanthology.org/emnlp22-frontmatter/2023.findings-acl.766.pdf
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