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While end-to-end neural machine translation (NMT) has made remarkableprogress recently, it still suffers from the data scarcity problem forlow-resource language pairs and domains. In this paper, we propose a method forzero-resource NMT by assuming that parallel sentences have close probabilitiesof generating a sentence in a third language. Based on this assumption, ourmethod is able to train a source-to-target NMT model ("student") withoutparallel corpora available, guided by an existing pivot-to-target NMT model("teacher") on a source-pivot parallel corpus. Experimental results show thatthe proposed method significantly improves over a baseline pivot-based model by+3.0 BLEU points across various language pairs.

A Teacher-Student Framework for Zero-Resource Neural Machine Translation