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        <title>Incorporating Syntax and Frame Semantics in Neural Network for Machine Reading Comprehension</title>
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    <name type="personal">
        <namePart type="given">Shaoru</namePart>
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    <name type="personal">
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    <name type="personal">
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    <name type="personal">
        <namePart type="given">Hongye</namePart>
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    <abstract>Machine reading comprehension (MRC) is one of the most critical yet challenging tasks in natural language understanding(NLU), where both syntax and semantics information of text are essential components for text understanding. It is surprising that jointly considering syntax and semantics in neural networks was never formally reported in literature. This paper makes the first attempt by proposing a novel Syntax and Frame Semantics model for Machine Reading Comprehension (SS-MRC), which takes full advantage of syntax and frame semantics to get richer text representation. Our extensive experimental results demonstrate that SS-MRC performs better than ten state-of-the-art technologies on machine reading comprehension task.</abstract>
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%0 Conference Proceedings
%T Incorporating Syntax and Frame Semantics in Neural Network for Machine Reading Comprehension
%A Guo, Shaoru
%A Guan, Yong
%A Li, Ru
%A Li, Xiaoli
%A Tan, Hongye
%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 guo-etal-2020-incorporating
%X Machine reading comprehension (MRC) is one of the most critical yet challenging tasks in natural language understanding(NLU), where both syntax and semantics information of text are essential components for text understanding. It is surprising that jointly considering syntax and semantics in neural networks was never formally reported in literature. This paper makes the first attempt by proposing a novel Syntax and Frame Semantics model for Machine Reading Comprehension (SS-MRC), which takes full advantage of syntax and frame semantics to get richer text representation. Our extensive experimental results demonstrate that SS-MRC performs better than ten state-of-the-art technologies on machine reading comprehension task.
%R 10.18653/v1/2020.coling-main.237
%U https://aclanthology.org/2020.coling-main.237
%U https://doi.org/10.18653/v1/2020.coling-main.237
%P 2635-2641

Markdown (Informal)

[Incorporating Syntax and Frame Semantics in Neural Network for Machine Reading Comprehension](https://aclanthology.org/2020.coling-main.237) (Guo et al., COLING 2020)

• Incorporating Syntax and Frame Semantics in Neural Network for Machine Reading Comprehension (Guo et al., COLING 2020)

ACL

• Shaoru Guo, Yong Guan, Ru Li, Xiaoli Li, and Hongye Tan. 2020. Incorporating Syntax and Frame Semantics in Neural Network for Machine Reading Comprehension. In Proceedings of the 28th International Conference on Computational Linguistics, pages 2635–2641, Barcelona, Spain (Online). International Committee on Computational Linguistics.