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        <title>A Document-Level Neural Machine Translation Model with Dynamic Caching Guided by Theme-Rheme Information</title>
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    <name type="personal">
        <namePart type="given">Yiqi</namePart>
        <namePart type="family">Tong</namePart>
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    <name type="personal">
        <namePart type="given">Jiangbin</namePart>
        <namePart type="family">Zheng</namePart>
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    <name type="personal">
        <namePart type="given">Yidong</namePart>
        <namePart type="family">Chen</namePart>
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    <name type="personal">
        <namePart type="given">Xiaodong</namePart>
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            <namePart type="given">Chengqing</namePart>
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    <abstract>Research on document-level Neural Machine Translation (NMT) models has attracted increasing attention in recent years. Although the proposed works have proved that the inter-sentence information is helpful for improving the performance of the NMT models, what information should be regarded as context remains ambiguous. To solve this problem, we proposed a novel cache-based document-level NMT model which conducts dynamic caching guided by theme-rheme information. The experiments on NIST evaluation sets demonstrate that our proposed model achieves substantial improvements over the state-of-the-art baseline NMT models. As far as we know, we are the first to introduce theme-rheme theory into the field of machine translation.</abstract>
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%0 Conference Proceedings
%T A Document-Level Neural Machine Translation Model with Dynamic Caching Guided by Theme-Rheme Information
%A Tong, Yiqi
%A Zheng, Jiangbin
%A Zhu, Hongkang
%A Chen, Yidong
%A Shi, Xiaodong
%Y Scott, Donia
%Y Bel, Nuria
%Y Zong, Chengqing
%S Proceedings of the 28th International Conference on Computational Linguistics
%D 2020
%8 December
%I International Committee on Computational Linguistics
%C Barcelona, Spain (Online)
%F tong-etal-2020-document
%X Research on document-level Neural Machine Translation (NMT) models has attracted increasing attention in recent years. Although the proposed works have proved that the inter-sentence information is helpful for improving the performance of the NMT models, what information should be regarded as context remains ambiguous. To solve this problem, we proposed a novel cache-based document-level NMT model which conducts dynamic caching guided by theme-rheme information. The experiments on NIST evaluation sets demonstrate that our proposed model achieves substantial improvements over the state-of-the-art baseline NMT models. As far as we know, we are the first to introduce theme-rheme theory into the field of machine translation.
%R 10.18653/v1/2020.coling-main.388
%U https://aclanthology.org/2020.coling-main.388
%U https://doi.org/10.18653/v1/2020.coling-main.388
%P 4385-4395

Markdown (Informal)

[A Document-Level Neural Machine Translation Model with Dynamic Caching Guided by Theme-Rheme Information](https://aclanthology.org/2020.coling-main.388) (Tong et al., COLING 2020)

• A Document-Level Neural Machine Translation Model with Dynamic Caching Guided by Theme-Rheme Information (Tong et al., COLING 2020)

ACL

• Yiqi Tong, Jiangbin Zheng, Hongkang Zhu, Yidong Chen, and Xiaodong Shi. 2020. A Document-Level Neural Machine Translation Model with Dynamic Caching Guided by Theme-Rheme Information. In Proceedings of the 28th International Conference on Computational Linguistics, pages 4385–4395, Barcelona, Spain (Online). International Committee on Computational Linguistics.