Although unsupervised neural machine translation (UNMT) has achieved success
in many language pairs, the copying problem, i.e., directly copying some parts
of the input sentence as the translation, is common among distant language
pairs, especially when low-resource languages are involved. We find this issue
is closely related to an unexpected copying behavior during online
back-translation (BT). In this work, we propose a simple but effective training
schedule that incorporates a language discriminator loss. The loss imposes
constraints on the intermediate translation so that the translation is in the
desired language. By conducting extensive experiments on different language
pairs, including similar and distant, high and low-resource languages, we find
that our method alleviates the copying problem, thus improving the translation
performance on low-resource languages.

本文提出一种简单且有效的训练方法，通过增加语言鉴别器的损失，来约束中间翻译结果的语言，从而减轻低资源语言翻译中常见的抄袭问题，并提升翻译表现。