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29 March 2024
 
  » arxiv » 1705.0753

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A Teacher-Student Framework for Zero-Resource Neural Machine Translation
Yun Chen ; Yang Liu ; Yong Cheng ; Victor O.K. Li ;
Date 2 May 2017
AbstractWhile 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 sentences have 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.
Source arXiv, 1705.0753
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