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.

翻译：多语言神经机器翻译模型具备零样本翻译的潜力，即能直接翻译训练中未见过的语言对。为促使监督方向向零样本方向的有效迁移，多语言神经机器翻译模型需学习跨语言的通用表征。本文提出一种跨语言一致性正则化方法CrossConST，旨在缩小不同语言间的表征差异，提升零样本翻译性能。理论分析表明，CrossConST隐式最大化零样本翻译的概率分布；低资源和高资源基准实验均证实，CrossConST能持续提升翻译性能。实验分析还证明，CrossConST能弥合句子表征差异并更好地对齐表征空间。鉴于CrossConST的普适性与简洁性，我们相信其可为未来多语言神经机器翻译研究提供强基线。