Improving Zero-shot Multilingual Neural Machine Translation by
  Leveraging Cross-lingual Consistency Regularization
Pengzhi Gao Liwen Zhang Zhongjun He Hua Wu Haifeng Wang
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.