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


While end-to-end neural machine translation (NMT) has made remarkable progress recently, it still suffers from the data scarcity problem for low-resource language pairs and domains. In this paper, we propose a method for zero-resource NMT by assuming that parallel sentence shave close probabilities of generating a sentence in a third language. Based on this assumption, our method is able to train a source-to-target NMT model (“student”) without parallel corpora available, guided by an existing pivot-to-target NMT model (“teacher”) on a source-pivot parallel corpus. Experimental results show that the proposed method significantly improves over a baseline pivot-based model by +3.0 BLEU points across various language pairs.


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# Title Author Topic Medium Score
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13 ACL 2018 Highlights: Understanding Representations and Evaluation in More Challenging Settings Sebastian Ruder 9999
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15 Advances in Neural Machine Translation Rico Sennrich, Alexandra Birch, Marcin Junczys-Dowmunt 1197
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