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        <title>Document-Level Neural Machine Translation with Hierarchical Attention Networks</title>
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        <namePart type="given">Lesly</namePart>
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        <namePart type="given">Dhananjay</namePart>
        <namePart type="family">Ram</namePart>
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        <namePart type="given">James</namePart>
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            <title>Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing</title>
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            <namePart type="given">Ellen</namePart>
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            <namePart type="given">Jun’ichi</namePart>
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    <abstract>Neural Machine Translation (NMT) can be improved by including document-level contextual information. For this purpose, we propose a hierarchical attention model to capture the context in a structured and dynamic manner. The model is integrated in the original NMT architecture as another level of abstraction, conditioning on the NMT model’s own previous hidden states. Experiments show that hierarchical attention significantly improves the BLEU score over a strong NMT baseline with the state-of-the-art in context-aware methods, and that both the encoder and decoder benefit from context in complementary ways.</abstract>
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%0 Conference Proceedings
%T Document-Level Neural Machine Translation with Hierarchical Attention Networks
%A Miculicich, Lesly
%A Ram, Dhananjay
%A Pappas, Nikolaos
%A Henderson, James
%Y Riloff, Ellen
%Y Chiang, David
%Y Hockenmaier, Julia
%Y Tsujii, Jun’ichi
%S Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
%D 2018
%8 oct nov
%I Association for Computational Linguistics
%C Brussels, Belgium
%F miculicich-etal-2018-document
%X Neural Machine Translation (NMT) can be improved by including document-level contextual information. For this purpose, we propose a hierarchical attention model to capture the context in a structured and dynamic manner. The model is integrated in the original NMT architecture as another level of abstraction, conditioning on the NMT model’s own previous hidden states. Experiments show that hierarchical attention significantly improves the BLEU score over a strong NMT baseline with the state-of-the-art in context-aware methods, and that both the encoder and decoder benefit from context in complementary ways.
%R 10.18653/v1/D18-1325
%U https://aclanthology.org/D18-1325/
%U https://doi.org/10.18653/v1/D18-1325
%P 2947-2954

Markdown (Informal)

[Document-Level Neural Machine Translation with Hierarchical Attention Networks](https://aclanthology.org/D18-1325/) (Miculicich et al., EMNLP 2018)

• Document-Level Neural Machine Translation with Hierarchical Attention Networks (Miculicich et al., EMNLP 2018)

ACL

• Lesly Miculicich, Dhananjay Ram, Nikolaos Pappas, and James Henderson. 2018. Document-Level Neural Machine Translation with Hierarchical Attention Networks. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 2947–2954, Brussels, Belgium. Association for Computational Linguistics.