Fan, Chuang, Yuan, Chaofa, Du, Jiachen, Gui, Lin, Yang, Min and Xu, Ruifeng (2020) Transition-based directed graph construction for emotion-cause pair extraction. In: 2020 Annual Conference of the Association for Computational Linguistics, 5-10 Jul 2020 . Published in: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics pp. 3707-3717. doi:10.18653/v1/2020.acl-main.342

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Abstract

Emotion-cause pair extraction aims to extract all potential pairs of emotions and corresponding causes from unannotated emotion text. Most existing methods are pipelined framework, which identifies emotions and extracts causes separately, leading to a drawback of error propagation. Towards this issue, we propose a transition-based model to transform the task into a procedure of parsing-like directed graph construction. The proposed model incrementally generates the directed graph with labeled edges based on a sequence of actions, from which we can recognize emotions with the corresponding causes simultaneously, thereby optimizing separate subtasks jointly and maximizing mutual benefits of tasks interdependently. Experimental results show that our approach achieves the best performance, outperforming the state-of-the-art methods by 6.71% (p<0.01) in F1 measure.

Item Type:	Conference Item (Paper)
Subjects:	P Language and Literature > P Philology. Linguistics Q Science > Q Science (General) Q Science > QA Mathematics Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software
Divisions:	Faculty of Science, Engineering and Medicine > Science > Computer Science
Library of Congress Subject Headings (LCSH):	Language and logic, Computational linguistics, Natural language processing (Computer science), Machine learning
Journal or Publication Title:	Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
Publisher:	Association for Computational Linguistics
Official Date:	July 2020
Dates:	Date Event July 2020 Published 14 April 2020 Accepted
Page Range:	pp. 3707-3717
DOI:	10.18653/v1/2020.acl-main.342
Status:	Peer Reviewed
Publication Status:	Published
Access rights to Published version:	Restricted or Subscription Access
Date of first compliant deposit:	12 May 2020
Date of first compliant Open Access:	27 August 2020
RIOXX Funder/Project Grant:	Project/Grant ID RIOXX Funder Name Funder ID 794196 Horizon 2020 Framework Programme http://dx.doi.org/10.13039/100010661 61632011 [NSFC] National Natural Science Foundation of China http://dx.doi.org/10.13039/501100001809 61906185 [NSFC] National Natural Science Foundation of China http://dx.doi.org/10.13039/501100001809 61876053 [NSFC] National Natural Science Foundation of China http://dx.doi.org/10.13039/501100001809 JCYJ20180507183527919 Science, Technology and Innovation Commission of Shenzhen Municipality http://dx.doi.org/10.13039/501100010877 JCYJ20180507183608379 Science, Technology and Innovation Commission of Shenzhen Municipality http://dx.doi.org/10.13039/501100010877 JSGG20170817140856618 Shenzhen Technology Development Program http://dx.doi.org/10.13039/501100012272 AWS13C008 People's Liberation Army University of Science and Technology http://dx.doi.org/10.13039/501100009573
Conference Paper Type:	Paper
Title of Event:	2020 Annual Conference of the Association for Computational Linguistics
Type of Event:	Conference
Date(s) of Event:	5-10 Jul 2020
Related URLs:	Publisher Organisation

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