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        <title>Exploiting Cross-Sentence Context for Neural Machine Translation</title>
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    <abstract>In translation, considering the document as a whole can help to resolve ambiguities and inconsistencies. In this paper, we propose a cross-sentence context-aware approach and investigate the influence of historical contextual information on the performance of neural machine translation (NMT). First, this history is summarized in a hierarchical way. We then integrate the historical representation into NMT in two strategies: 1) a warm-start of encoder and decoder states, and 2) an auxiliary context source for updating decoder states. Experimental results on a large Chinese-English translation task show that our approach significantly improves upon a strong attention-based NMT system by up to +2.1 BLEU points.</abstract>
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%0 Conference Proceedings
%T Exploiting Cross-Sentence Context for Neural Machine Translation
%A Wang, Longyue
%A Tu, Zhaopeng
%A Way, Andy
%A Liu, Qun
%Y Palmer, Martha
%Y Hwa, Rebecca
%Y Riedel, Sebastian
%S Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
%D 2017
%8 September
%I Association for Computational Linguistics
%C Copenhagen, Denmark
%F wang-etal-2017-exploiting-cross
%X In translation, considering the document as a whole can help to resolve ambiguities and inconsistencies. In this paper, we propose a cross-sentence context-aware approach and investigate the influence of historical contextual information on the performance of neural machine translation (NMT). First, this history is summarized in a hierarchical way. We then integrate the historical representation into NMT in two strategies: 1) a warm-start of encoder and decoder states, and 2) an auxiliary context source for updating decoder states. Experimental results on a large Chinese-English translation task show that our approach significantly improves upon a strong attention-based NMT system by up to +2.1 BLEU points.
%R 10.18653/v1/D17-1301
%U https://aclanthology.org/D17-1301
%U https://doi.org/10.18653/v1/D17-1301
%P 2826-2831

Markdown (Informal)

[Exploiting Cross-Sentence Context for Neural Machine Translation](https://aclanthology.org/D17-1301) (Wang et al., EMNLP 2017)

• Exploiting Cross-Sentence Context for Neural Machine Translation (Wang et al., EMNLP 2017)

ACL

• Longyue Wang, Zhaopeng Tu, Andy Way, and Qun Liu. 2017. Exploiting Cross-Sentence Context for Neural Machine Translation. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 2826–2831, Copenhagen, Denmark. Association for Computational Linguistics.