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.

本文介绍了一种跨语言一致性正则化方法CrossConST，用于在多语言神经机器翻译模型中实现零-shot翻译，以实现不同语言之间的普适表示，并提高翻译性能。实验结果证明CrossConST能够提高模型的翻译性能并降低句子表示间的差距，为未来的多语言NMT研究奠定了基础。

利用跨语言一致性正则化提升零样本多语言神经机器翻译