Multilingual Machine Translation (MMT) benefits from knowledge transfer
across different language pairs. However, improvements in one-to-many
translation compared to many-to-one translation are only marginal and sometimes
even negligible. This performance discrepancy raises the question of to what
extent positive transfer plays a role on the target-side for one-to-many MT. In
this paper, we conduct a large-scale study that varies the auxiliary target
side languages along two dimensions, i.e., linguistic similarity and corpus
size, to show the dynamic impact of knowledge transfer on the main language
pairs. We show that linguistically similar auxiliary target languages exhibit
strong ability to transfer positive knowledge. With an increasing size of
similar target languages, the positive transfer is further enhanced to benefit
the main language pairs. Meanwhile, we find distant auxiliary target languages
can also unexpectedly benefit main language pairs, even with minimal positive
transfer ability. Apart from transfer, we show distant auxiliary target
languages can act as a regularizer to benefit translation performance by
enhancing the generalization and model inference calibration.

多语言机器翻译（MMT）通过不同语言对之间的知识迁移而受益。本文通过在目标辅助语言方面进行大规模研究，涉及语言相似性和语料库大小等两个维度，展示了知识迁移对主要语言对的动态影响。我们发现，在语言相似的辅助目标语言中，存在强烈的正向知识迁移能力，并且随着相似目标语言规模的增加，正向迁移效果进一步增强，有利于主要语言对。与此同时，我们发现远距离的辅助目标语言即使拥有较小的正向迁移能力，也能意外地对主要语言对产生正面影响。此外，我们还展示了远距离的辅助目标语言可以作为一种正则化器，通过增强广义化和模型推理校正来提高翻译性能。