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

UM4: Unified Multilingual Multiple Teacher-Student Model for Zero-Resource Neural Machine Translation