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        <title>Diving Deep into Context-Aware Neural Machine Translation</title>
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        <namePart type="given">Jingjing</namePart>
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    <abstract>Context-aware neural machine translation (NMT) is a promising direction to improve the translation quality by making use of the additional context, e.g., document-level translation, or having meta-information. Although there exist various architectures and analyses, the effectiveness of different context-aware NMT models is not well explored yet. This paper analyzes the performance of document-level NMT models on four diverse domains with a varied amount of parallel document-level bilingual data. We conduct a comprehensive set of experiments to investigate the impact of document-level NMT. We find that there is no single best approach to document-level NMT, but rather that different architectures come out on top on different tasks. Looking at task-specific problems, such as pronoun resolution or headline translation, we find improvements in the context-aware systems, even in cases where the corpus-level metrics like BLEU show no significant improvement. We also show that document-level back-translation significantly helps to compensate for the lack of document-level bi-texts.</abstract>
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        <url>https://aclanthology.org/2020.wmt-1.71</url>
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        <date>2020-nov</date>
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%0 Conference Proceedings
%T Diving Deep into Context-Aware Neural Machine Translation
%A Huo, Jingjing
%A Herold, Christian
%A Gao, Yingbo
%A Dahlmann, Leonard
%A Khadivi, Shahram
%A Ney, Hermann
%S Proceedings of the Fifth Conference on Machine Translation
%D 2020
%8 nov
%I Association for Computational Linguistics
%C Online
%F huo-etal-2020-diving
%X Context-aware neural machine translation (NMT) is a promising direction to improve the translation quality by making use of the additional context, e.g., document-level translation, or having meta-information. Although there exist various architectures and analyses, the effectiveness of different context-aware NMT models is not well explored yet. This paper analyzes the performance of document-level NMT models on four diverse domains with a varied amount of parallel document-level bilingual data. We conduct a comprehensive set of experiments to investigate the impact of document-level NMT. We find that there is no single best approach to document-level NMT, but rather that different architectures come out on top on different tasks. Looking at task-specific problems, such as pronoun resolution or headline translation, we find improvements in the context-aware systems, even in cases where the corpus-level metrics like BLEU show no significant improvement. We also show that document-level back-translation significantly helps to compensate for the lack of document-level bi-texts.
%U https://aclanthology.org/2020.wmt-1.71
%P 604-616

Markdown (Informal)

[Diving Deep into Context-Aware Neural Machine Translation](https://aclanthology.org/2020.wmt-1.71) (Huo et al., WMT 2020)

• Diving Deep into Context-Aware Neural Machine Translation (Huo et al., WMT 2020)

ACL

• Jingjing Huo, Christian Herold, Yingbo Gao, Leonard Dahlmann, Shahram Khadivi, and Hermann Ney. 2020. Diving Deep into Context-Aware Neural Machine Translation. In Proceedings of the Fifth Conference on Machine Translation, pages 604–616, Online. Association for Computational Linguistics.