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        <title>A Frame-based Sentence Representation for Machine Reading Comprehension</title>
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        <namePart type="given">Shaoru</namePart>
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    <abstract>Sentence representation (SR) is the most crucial and challenging task in Machine Reading Comprehension (MRC). MRC systems typically only utilize the information contained in the sentence itself, while human beings can leverage their semantic knowledge. To bridge the gap, we proposed a novel Frame-based Sentence Representation (FSR) method, which employs frame semantic knowledge to facilitate sentence modelling. Specifically, different from existing methods that only model lexical units (LUs), Frame Representation Models, which utilize both LUs in frame and Frame-to-Frame (F-to-F) relations, are designed to model frames and sentences with attention schema. Our proposed FSR method is able to integrate multiple-frame semantic information to get much better sentence representations. Our extensive experimental results show that it performs better than state-of-the-art technologies on machine reading comprehension task.</abstract>
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        <date>2020-jul</date>
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%0 Conference Proceedings
%T A Frame-based Sentence Representation for Machine Reading Comprehension
%A Guo, Shaoru
%A Li, Ru
%A Tan, Hongye
%A Li, Xiaoli
%A Guan, Yong
%A Zhao, Hongyan
%A Zhang, Yueping
%S Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
%D 2020
%8 jul
%I Association for Computational Linguistics
%C Online
%F guo-etal-2020-frame
%X Sentence representation (SR) is the most crucial and challenging task in Machine Reading Comprehension (MRC). MRC systems typically only utilize the information contained in the sentence itself, while human beings can leverage their semantic knowledge. To bridge the gap, we proposed a novel Frame-based Sentence Representation (FSR) method, which employs frame semantic knowledge to facilitate sentence modelling. Specifically, different from existing methods that only model lexical units (LUs), Frame Representation Models, which utilize both LUs in frame and Frame-to-Frame (F-to-F) relations, are designed to model frames and sentences with attention schema. Our proposed FSR method is able to integrate multiple-frame semantic information to get much better sentence representations. Our extensive experimental results show that it performs better than state-of-the-art technologies on machine reading comprehension task.
%R 10.18653/v1/2020.acl-main.83
%U https://aclanthology.org/2020.acl-main.83
%U https://doi.org/10.18653/v1/2020.acl-main.83
%P 891-896

Markdown (Informal)

[A Frame-based Sentence Representation for Machine Reading Comprehension](https://aclanthology.org/2020.acl-main.83) (Guo et al., ACL 2020)

• A Frame-based Sentence Representation for Machine Reading Comprehension (Guo et al., ACL 2020)

ACL

• Shaoru Guo, Ru Li, Hongye Tan, Xiaoli Li, Yong Guan, Hongyan Zhao, and Yueping Zhang. 2020. A Frame-based Sentence Representation for Machine Reading Comprehension. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 891–896, Online. Association for Computational Linguistics.