Unsupervised on-the-fly back-translation, in conjunction with multilingual
pretraining, is the dominant method for unsupervised neural machine
translation. Theoretically, however, the method should not work in general. We
therefore conduct controlled experiments with artificial languages to determine
what properties of languages make back-translation an effective training
method, covering lexical, syntactic, and semantic properties. We find, contrary
to popular belief, that (i) parallel word frequency distributions, (ii)
partially shared vocabulary, and (iii) similar syntactic structure across
languages are not sufficient to explain the success of back-translation. We
show however that even crude semantic signal (similar lexical fields across
languages) does improve alignment of two languages through back-translation. We
conjecture that rich semantic dependencies, parallel across languages, are at
the root of the success of unsupervised methods based on back-translation.
Overall, the success of unsupervised machine translation was far from being
analytically guaranteed. Instead, it is another proof that languages of the
world share deep similarities, and we hope to show how to identify which of
these similarities can serve the development of unsupervised, cross-linguistic
tools.

无监督神经机器翻译中的实时反向翻译与多语言预训练是主要方法，该论文通过人工语言的实验，发现词汇、句法和语义属性并不能充分解释反向翻译成功的原因，但是语义信号能通过反向翻译改进两种语言的对齐，推测多语言间存在平行的丰富语义依赖，这证明了无监督机器翻译的成功并非分析性的保证，并希望能够确定哪些共同性可用于无监督、跨语言工具的开发。