  Most translation tasks among languages belong to the zero-resource
translation problem where parallel corpora are unavailable. Multilingual neural
machine translation (MNMT) enables one-pass translation using shared semantic
space for all languages compared to the two-pass pivot translation but often
underperforms the pivot-based method. In this paper, we propose a novel method,
named as Unified Multilingual Multiple teacher-student Model for NMT (UM4). Our
method unifies source-teacher, target-teacher, and pivot-teacher models to
guide the student model for the zero-resource translation. The source teacher
and target teacher force the student to learn the direct source to target
translation by the distilled knowledge on both source and target sides. The
monolingual corpus is further leveraged by the pivot-teacher model to enhance
the student model. Experimental results demonstrate that our model of 72
directions significantly outperforms previous methods on the WMT benchmark.
