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        <title>Enhanced Sentence Alignment Network for Efficient Short Text Matching</title>
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    <name type="personal">
        <namePart type="given">Zhe</namePart>
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        <namePart type="given">Zuohui</namePart>
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        <namePart type="given">Weiwei</namePart>
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            <title>Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020)</title>
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            <namePart type="given">Wei</namePart>
            <namePart type="family">Xu</namePart>
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            <namePart type="given">Alan</namePart>
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    <abstract>Cross-sentence attention has been widely applied in text matching, in which model learns the aligned information between two intermediate sequence representations to capture their semantic relationship. However, commonly the intermediate representations are generated solely based on the preceding layers and the models may suffer from error propagation and unstable matching, especially when multiple attention layers are used. In this paper, we pro-pose an enhanced sentence alignment network with simple gated feature augmentation, where the model is able to flexibly integrate both original word and contextual features to improve the cross-sentence attention. Moreover, our model is less complex with fewer parameters compared to many state-of-the-art structures. Experiments on three benchmark datasets validate our model capacity for text matching.</abstract>
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            <start>34</start>
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%0 Conference Proceedings
%T Enhanced Sentence Alignment Network for Efficient Short Text Matching
%A Hu, Zhe
%A Fu, Zuohui
%A Peng, Cheng
%A Wang, Weiwei
%Y Xu, Wei
%Y Ritter, Alan
%Y Baldwin, Tim
%Y Rahimi, Afshin
%S Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020)
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F hu-etal-2020-enhanced
%X Cross-sentence attention has been widely applied in text matching, in which model learns the aligned information between two intermediate sequence representations to capture their semantic relationship. However, commonly the intermediate representations are generated solely based on the preceding layers and the models may suffer from error propagation and unstable matching, especially when multiple attention layers are used. In this paper, we pro-pose an enhanced sentence alignment network with simple gated feature augmentation, where the model is able to flexibly integrate both original word and contextual features to improve the cross-sentence attention. Moreover, our model is less complex with fewer parameters compared to many state-of-the-art structures. Experiments on three benchmark datasets validate our model capacity for text matching.
%R 10.18653/v1/2020.wnut-1.6
%U https://aclanthology.org/2020.wnut-1.6
%U https://doi.org/10.18653/v1/2020.wnut-1.6
%P 34-40

Markdown (Informal)

[Enhanced Sentence Alignment Network for Efficient Short Text Matching](https://aclanthology.org/2020.wnut-1.6) (Hu et al., WNUT 2020)

• Enhanced Sentence Alignment Network for Efficient Short Text Matching (Hu et al., WNUT 2020)

ACL

• Zhe Hu, Zuohui Fu, Cheng Peng, and Weiwei Wang. 2020. Enhanced Sentence Alignment Network for Efficient Short Text Matching. In Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020), pages 34–40, Online. Association for Computational Linguistics.