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    <titleInfo>
        <title>Dynamic-FACT: A Dynamic Framework for Adaptive Context-Aware Translation</title>
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    <name type="personal">
        <namePart type="given">Chen</namePart>
        <namePart type="family">Linqing</namePart>
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        <titleInfo>
            <title>Proceedings of the 22nd Chinese National Conference on Computational Linguistics</title>
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        <name type="personal">
            <namePart type="given">Maosong</namePart>
            <namePart type="family">Sun</namePart>
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        <name type="personal">
            <namePart type="given">Bing</namePart>
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            <namePart type="given">Xipeng</namePart>
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            <namePart type="given">Jing</namePart>
            <namePart type="family">Jiang</namePart>
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        <name type="personal">
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            <namePart type="family">Han</namePart>
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            <publisher>Chinese Information Processing Society of China</publisher>
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    <abstract>“Document-level neural machine translation (NMT) has garnered considerable attention sincethe emergence of various context-aware NMT models. However, these static NMT models aretrained on fixed parallel datasets, thus lacking awareness of the target document during infer-ence. In order to alleviate this limitation, we propose a dynamic adapter-translator frameworkfor context-aware NMT, which adapts the trained NMT model to the input document prior totranslation. Specifically, the document adapter reconstructs the scrambled portion of the originaldocument from a deliberately corrupted version, thereby reducing the performance disparity be-tween training and inference. To achieve this, we employ an adaptation process in both the train-ing and inference stages. Our experimental results on document-level translation benchmarksdemonstrate significant enhancements in translation performance, underscoring the necessity ofdynamic adaptation for context-aware translation and the efficacy of our methodologies. Introduction”</abstract>
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        <url>https://aclanthology.org/2023.ccl-1.57</url>
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        <date>2023-08</date>
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            <start>665</start>
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%0 Conference Proceedings
%T Dynamic-FACT: A Dynamic Framework for Adaptive Context-Aware Translation
%A Linqing, Chen
%A Weilei, Wang
%Y Sun, Maosong
%Y Qin, Bing
%Y Qiu, Xipeng
%Y Jiang, Jing
%Y Han, Xianpei
%S Proceedings of the 22nd Chinese National Conference on Computational Linguistics
%D 2023
%8 August
%I Chinese Information Processing Society of China
%C Harbin, China
%G English
%F linqing-weilei-2023-dynamic
%X “Document-level neural machine translation (NMT) has garnered considerable attention sincethe emergence of various context-aware NMT models. However, these static NMT models aretrained on fixed parallel datasets, thus lacking awareness of the target document during infer-ence. In order to alleviate this limitation, we propose a dynamic adapter-translator frameworkfor context-aware NMT, which adapts the trained NMT model to the input document prior totranslation. Specifically, the document adapter reconstructs the scrambled portion of the originaldocument from a deliberately corrupted version, thereby reducing the performance disparity be-tween training and inference. To achieve this, we employ an adaptation process in both the train-ing and inference stages. Our experimental results on document-level translation benchmarksdemonstrate significant enhancements in translation performance, underscoring the necessity ofdynamic adaptation for context-aware translation and the efficacy of our methodologies. Introduction”
%U https://aclanthology.org/2023.ccl-1.57
%P 665-676

Markdown (Informal)

[Dynamic-FACT: A Dynamic Framework for Adaptive Context-Aware Translation](https://aclanthology.org/2023.ccl-1.57) (Linqing & Weilei, CCL 2023)

• Dynamic-FACT: A Dynamic Framework for Adaptive Context-Aware Translation (Linqing & Weilei, CCL 2023)

ACL

• Chen Linqing and Wang Weilei. 2023. Dynamic-FACT: A Dynamic Framework for Adaptive Context-Aware Translation. In Proceedings of the 22nd Chinese National Conference on Computational Linguistics, pages 665–676, Harbin, China. Chinese Information Processing Society of China.