Multilingual Neural Machine Translation (MNMT) has aroused widespread
interest due to its efficiency. An exciting advantage of MNMT models is that
they could also translate between unsupervised (zero-shot) language directions.
Language tag (LT) strategies are often adopted to indicate the translation
directions in MNMT. In this paper, we demonstrate that the LTs are not only
indicators for translation directions but also crucial to zero-shot translation
qualities. Unfortunately, previous work tends to ignore the importance of LT
strategies. We demonstrate that a proper LT strategy could enhance the
consistency of semantic representations and alleviate the off-target issue in
zero-shot directions. Experimental results show that by ignoring the source
language tag (SLT) and adding the target language tag (TLT) to the encoder, the
zero-shot translations could achieve a +8 BLEU score difference over other LT
strategies in IWSLT17, Europarl, TED talks translation tasks.

本文探讨了多语言神经机器翻译的语言标记策略对无监督语言翻译性能的影响，实验表明在源语言标记基础上加入目标语言标记可以提高语义表征的一致性和减轻 “偏离目标” 的问题，使得零样本翻译在 IWSLT17，Europarl 和 TED talks 任务中获得了比其他语言标记策略高 8 个 BLEU 评分。