A Frame-based Sentence Representation for Machine Reading Comprehension

Shaoru Guo, Ru Li, Hongye Tan, Xiaoli Li, Yong Guan, Hongyan Zhao, Yueping Zhang


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.
Anthology ID:
2020.acl-main.83
Volume:
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2020
Address:
Online
Editors:
Dan Jurafsky, Joyce Chai, Natalie Schluter, Joel Tetreault
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
891–896
Language:
URL:
https://aclanthology.org/2020.acl-main.83
DOI:
10.18653/v1/2020.acl-main.83
Bibkey:
Cite (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.
Cite (Informal):
A Frame-based Sentence Representation for Machine Reading Comprehension (Guo et al., ACL 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.acl-main.83.pdf
Video:
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