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        <title>Modularized Syntactic Neural Networks for Sentence Classification</title>
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    <name type="personal">
        <namePart type="given">Haiyan</namePart>
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    <name type="personal">
        <namePart type="given">Ying</namePart>
        <namePart type="family">Liu</namePart>
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    <name type="personal">
        <namePart type="given">Shaoyun</namePart>
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            <title>Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)</title>
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            <namePart type="given">Bonnie</namePart>
            <namePart type="family">Webber</namePart>
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        <name type="personal">
            <namePart type="given">Trevor</namePart>
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            <namePart type="given">Yang</namePart>
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    <abstract>This paper focuses on tree-based modeling for the sentence classification task. In existing works, aggregating on a syntax tree usually considers local information of sub-trees. In contrast, in addition to the local information, our proposed Modularized Syntactic Neural Network (MSNN) utilizes the syntax category labels and takes advantage of the global context while modeling sub-trees. In MSNN, each node of a syntax tree is modeled by a label-related syntax module. Each syntax module aggregates the outputs of lower-level modules, and finally, the root module provides the sentence representation. We design a tree-parallel mini-batch strategy for efficient training and predicting. Experimental results on four benchmark datasets show that our MSNN significantly outperforms previous state-of-the-art tree-based methods on the sentence classification task.</abstract>
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    <identifier type="doi">10.18653/v1/2020.emnlp-main.222</identifier>
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        <date>2020-11</date>
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%0 Conference Proceedings
%T Modularized Syntactic Neural Networks for Sentence Classification
%A Wu, Haiyan
%A Liu, Ying
%A Shi, Shaoyun
%Y Webber, Bonnie
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F wu-etal-2020-modularized
%X This paper focuses on tree-based modeling for the sentence classification task. In existing works, aggregating on a syntax tree usually considers local information of sub-trees. In contrast, in addition to the local information, our proposed Modularized Syntactic Neural Network (MSNN) utilizes the syntax category labels and takes advantage of the global context while modeling sub-trees. In MSNN, each node of a syntax tree is modeled by a label-related syntax module. Each syntax module aggregates the outputs of lower-level modules, and finally, the root module provides the sentence representation. We design a tree-parallel mini-batch strategy for efficient training and predicting. Experimental results on four benchmark datasets show that our MSNN significantly outperforms previous state-of-the-art tree-based methods on the sentence classification task.
%R 10.18653/v1/2020.emnlp-main.222
%U https://aclanthology.org/2020.emnlp-main.222
%U https://doi.org/10.18653/v1/2020.emnlp-main.222
%P 2786-2792

Markdown (Informal)

[Modularized Syntactic Neural Networks for Sentence Classification](https://aclanthology.org/2020.emnlp-main.222) (Wu et al., EMNLP 2020)

• Modularized Syntactic Neural Networks for Sentence Classification (Wu et al., EMNLP 2020)

ACL

• Haiyan Wu, Ying Liu, and Shaoyun Shi. 2020. Modularized Syntactic Neural Networks for Sentence Classification. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 2786–2792, Online. Association for Computational Linguistics.