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        <title>When and Why is Document-level Context Useful in Neural Machine Translation?</title>
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        <namePart type="given">Yunsu</namePart>
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            <title>Proceedings of the Fourth Workshop on Discourse in Machine Translation (DiscoMT 2019)</title>
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    <abstract>Document-level context has received lots of attention for compensating neural machine translation (NMT) of isolated sentences. However, recent advances in document-level NMT focus on sophisticated integration of the context, explaining its improvement with only a few selected examples or targeted test sets. We extensively quantify the causes of improvements by a document-level model in general test sets, clarifying the limit of the usefulness of document-level context in NMT. We show that most of the improvements are not interpretable as utilizing the context. We also show that a minimal encoding is sufficient for the context modeling and very long context is not helpful for NMT.</abstract>
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        <date>2019-11</date>
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%0 Conference Proceedings
%T When and Why is Document-level Context Useful in Neural Machine Translation?
%A Kim, Yunsu
%A Tran, Duc Thanh
%A Ney, Hermann
%Y Popescu-Belis, Andrei
%Y Loáiciga, Sharid
%Y Hardmeier, Christian
%Y Xiong, Deyi
%S Proceedings of the Fourth Workshop on Discourse in Machine Translation (DiscoMT 2019)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F kim-etal-2019-document
%X Document-level context has received lots of attention for compensating neural machine translation (NMT) of isolated sentences. However, recent advances in document-level NMT focus on sophisticated integration of the context, explaining its improvement with only a few selected examples or targeted test sets. We extensively quantify the causes of improvements by a document-level model in general test sets, clarifying the limit of the usefulness of document-level context in NMT. We show that most of the improvements are not interpretable as utilizing the context. We also show that a minimal encoding is sufficient for the context modeling and very long context is not helpful for NMT.
%R 10.18653/v1/D19-6503
%U https://aclanthology.org/D19-6503
%U https://doi.org/10.18653/v1/D19-6503
%P 24-34

Markdown (Informal)

[When and Why is Document-level Context Useful in Neural Machine Translation?](https://aclanthology.org/D19-6503) (Kim et al., DiscoMT 2019)

• When and Why is Document-level Context Useful in Neural Machine Translation? (Kim et al., DiscoMT 2019)

ACL

• Yunsu Kim, Duc Thanh Tran, and Hermann Ney. 2019. When and Why is Document-level Context Useful in Neural Machine Translation?. In Proceedings of the Fourth Workshop on Discourse in Machine Translation (DiscoMT 2019), pages 24–34, Hong Kong, China. Association for Computational Linguistics.