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

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

URL: http://arxiv.org/abs/2305.07310v1
Date: Fri, 12 May 2023 08:32:18 GMT
Title: Improving Zero-shot Multilingual Neural Machine Translation by Leveraging Cross-lingual Consistency Regularization
Authors: Pengzhi Gao, Liwen Zhang, Zhongjun He, Hua Wu, Haifeng Wang
Abstract summary: The multilingual neural machine translation (NMT) model has a promising capability of zero-shot translation. This paper introduces a cross-lingual consistency regularization, CrossConST, to bridge the representation gap among different languages.
Score: 46.09132547431629
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
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.

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