Multilingual Neural Machine Translation (NMT) models are capable of
translating between multiple source and target languages. Despite various
approaches to train such models, they have difficulty with zero-shot
translation: translating between language pairs that were not together seen
during training. In this paper we first diagnose why state-of-the-art
multilingual NMT models that rely purely on parameter sharing, fail to
generalize to unseen language pairs. We then propose auxiliary losses on the
NMT encoder that impose representational invariance across languages. Our
simple approach vastly improves zero-shot translation quality without
regressing on supervised directions. For the first time, on WMT14
English-FrenchGerman, we achieve zero-shot performance that is on par with
pivoting. We also demonstrate the easy scalability of our approach to multiple
languages on the IWSLT 2017 shared task.

本文研究了多语言神经机器翻译模型的零样本翻译问题，提出了基于辅助损失的方法，并在 WMT14 英语 - 法语 / 德语上实现了与基于中介语的模型相媲美的零样本翻译效果，同时在 IWSLT 2017 共享任务中验证了该方法的易于扩展性。