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。

零样本神经机器翻译中的缺失元素