  Neural machine translation~(NMT) is ineffective for zero-resource languages.
Recent works exploring the possibility of unsupervised neural machine
translation (UNMT) with only monolingual data can achieve promising results.
However, there are still big gaps between UNMT and NMT with parallel
supervision. In this work, we introduce a multilingual unsupervised NMT
(\method) framework to leverage weakly supervised signals from high-resource
language pairs to zero-resource translation directions. More specifically, for
unsupervised language pairs \texttt{En-De}, we can make full use of the
information from parallel dataset \texttt{En-Fr} to jointly train the
unsupervised translation directions all in one model. \method is based on
multilingual models which require no changes to the standard unsupervised NMT.
Empirical results demonstrate that \method significantly improves the
translation quality by more than 3 BLEU score on six benchmark unsupervised
translation directions.
