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        <title>Context-Aware Neural Machine Translation Learns Anaphora Resolution</title>
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        <namePart type="given">Elena</namePart>
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    <name type="personal">
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            <namePart type="family">Gurevych</namePart>
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            <namePart type="given">Yusuke</namePart>
            <namePart type="family">Miyao</namePart>
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    <abstract>Standard machine translation systems process sentences in isolation and hence ignore extra-sentential information, even though extended context can both prevent mistakes in ambiguous cases and improve translation coherence. We introduce a context-aware neural machine translation model designed in such way that the flow of information from the extended context to the translation model can be controlled and analyzed. We experiment with an English-Russian subtitles dataset, and observe that much of what is captured by our model deals with improving pronoun translation. We measure correspondences between induced attention distributions and coreference relations and observe that the model implicitly captures anaphora. It is consistent with gains for sentences where pronouns need to be gendered in translation. Beside improvements in anaphoric cases, the model also improves in overall BLEU, both over its context-agnostic version (+0.7) and over simple concatenation of the context and source sentences (+0.6).</abstract>
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    <identifier type="doi">10.18653/v1/P18-1117</identifier>
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%0 Conference Proceedings
%T Context-Aware Neural Machine Translation Learns Anaphora Resolution
%A Voita, Elena
%A Serdyukov, Pavel
%A Sennrich, Rico
%A Titov, Ivan
%Y Gurevych, Iryna
%Y Miyao, Yusuke
%S Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2018
%8 July
%I Association for Computational Linguistics
%C Melbourne, Australia
%F voita-etal-2018-context
%X Standard machine translation systems process sentences in isolation and hence ignore extra-sentential information, even though extended context can both prevent mistakes in ambiguous cases and improve translation coherence. We introduce a context-aware neural machine translation model designed in such way that the flow of information from the extended context to the translation model can be controlled and analyzed. We experiment with an English-Russian subtitles dataset, and observe that much of what is captured by our model deals with improving pronoun translation. We measure correspondences between induced attention distributions and coreference relations and observe that the model implicitly captures anaphora. It is consistent with gains for sentences where pronouns need to be gendered in translation. Beside improvements in anaphoric cases, the model also improves in overall BLEU, both over its context-agnostic version (+0.7) and over simple concatenation of the context and source sentences (+0.6).
%R 10.18653/v1/P18-1117
%U https://aclanthology.org/P18-1117
%U https://doi.org/10.18653/v1/P18-1117
%P 1264-1274

Markdown (Informal)

[Context-Aware Neural Machine Translation Learns Anaphora Resolution](https://aclanthology.org/P18-1117) (Voita et al., ACL 2018)

• Context-Aware Neural Machine Translation Learns Anaphora Resolution (Voita et al., ACL 2018)

ACL

• Elena Voita, Pavel Serdyukov, Rico Sennrich, and Ivan Titov. 2018. Context-Aware Neural Machine Translation Learns Anaphora Resolution. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1264–1274, Melbourne, Australia. Association for Computational Linguistics.