Exploiting a common language as an auxiliary for better translation has a
long tradition in machine translation and lets supervised learning-based
machine translation enjoy the enhancement delivered by the well-used pivot
language in the absence of a source language to target language parallel
corpus. The rise of unsupervised neural machine translation (UNMT) almost
completely relieves the parallel corpus curse, though UNMT is still subject to
unsatisfactory performance due to the vagueness of the clues available for its
core back-translation training. Further enriching the idea of pivot translation
by extending the use of parallel corpora beyond the source-target paradigm, we
propose a new reference language-based framework for UNMT, RUNMT, in which the
reference language only shares a parallel corpus with the source, but this
corpus still indicates a signal clear enough to help the reconstruction
training of UNMT through a proposed reference agreement mechanism. Experimental
results show that our methods improve the quality of UNMT over that of a strong
baseline that uses only one auxiliary language, demonstrating the usefulness of
the proposed reference language-based UNMT and establishing a good start for
the community.

本文提出了一种基于参考语言的非监督神经机器翻译 (RUNMT) 框架，以解决非监督翻译性能不佳的问题，通过扩充平行语料库的使用范围，使得参考语言只需要与源语言共享平行语料库，同时提出了一种参考协议机制，利用参考语言中的线索来帮助无监督机器翻译的重构训练。实验结果表明，该方法比只使用一个辅助语言的强基线模型在质量上有所提高，为该领域建立了良好的起点。