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        <title>A Teacher-Student Framework for Zero-Resource Neural Machine Translation</title>
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    <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 the 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.</abstract>
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    <identifier type="doi">10.18653/v1/P17-1176</identifier>
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%0 Conference Proceedings
%T A Teacher-Student Framework for Zero-Resource Neural Machine Translation
%A Chen, Yun
%A Liu, Yang
%A Cheng, Yong
%A Li, Victor O.K.
%Y Barzilay, Regina
%Y Kan, Min-Yen
%S Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2017
%8 July
%I Association for Computational Linguistics
%C Vancouver, Canada
%F chen-etal-2017-teacher
%X 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 the 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.
%R 10.18653/v1/P17-1176
%U https://aclanthology.org/P17-1176
%U https://doi.org/10.18653/v1/P17-1176
%P 1925-1935

Markdown (Informal)

[A Teacher-Student Framework for Zero-Resource Neural Machine Translation](https://aclanthology.org/P17-1176) (Chen et al., ACL 2017)

• A Teacher-Student Framework for Zero-Resource Neural Machine Translation (Chen et al., ACL 2017)

ACL

• Yun Chen, Yang Liu, Yong Cheng, and Victor O.K. Li. 2017. A Teacher-Student Framework for Zero-Resource Neural Machine Translation. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1925–1935, Vancouver, Canada. Association for Computational Linguistics.