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Article overview
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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 | Abstract: | 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 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 | Services: | Forum | Review | PDF | Favorites |
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