Quantum Probability-inspired Graph Attention Network for Modeling Complex Text Interaction

doi:10.1016/j.knosys.2021.107557

CORC > 自动化研究所 > 中国科学院自动化研究所 > 复杂系统管理与控制国家重点实验室 > 互联网大数据与安全信息学研究中心

	Quantum Probability-inspired Graph Attention Network for Modeling Complex Text Interaction
	Yan, Peng 1,3; Li, Linjing 1,2,3; Zeng, Daniel 1,2,3
刊名	KNOWLEDGE-BASED SYSTEMS
	2021-12-25
卷号	234 页码:12
关键词	Natural language processing Quantum probability Graph attention network
ISSN号	0950-7051
DOI	10.1016/j.knosys.2021.107557
通讯作者	Li, Linjing(linjing.li@ia.ac.cn)
英文摘要	Inspired by quantum-like phenomena in human language understanding, recent studies propose quan-tum probability-inspired neural networks to model natural language by treating words as superposition states and a sentence as a mixed state. However, many complex natural language processing tasks (e.g., emotion-cause pair extraction or joint dialog act recognition and sentiment classification) require modeling the complex and graphical interaction of multiple text pieces (e.g., multiple clauses in a document or multiple utterances in a dialog). The existing quantum probability-inspired neural networks only encode sequential interaction of a sequence of words, but cannot model the complex interaction of text pieces. To generalize the quantum framework from modeling word sequence to modeling complex and graphical text interaction, we propose a Quantum Probability-inspired Graph Attention NeTwork (QPGAT) by combining quantum probability and graph attention mechanism in a unified framework. Specifically, a text interaction graph is firstly constructed to describe the complex interaction of text pieces. Then QPGAT models each text node as a particle in a superposition state and each node's neighborhood in the graph as a mixed system in a mixed state to learn interaction-aware text node representations. We apply QPGAT to the two important and complex NLP tasks, emotion- cause pair extraction and joint dialog act recognition and sentiment classification. Experiment results show that QPGAT is competitive compared with the state-of-the-art methods on the two complex NLP tasks, demonstrating the effectiveness of QPGAT. Moreover, QPGAT can also provide a reasonable post-hoc explanation about the model decision process for emotion-cause pair extraction. (c) 2021 Elsevier B.V. All rights reserved.
WOS研究方向	Computer Science
语种	英语
出版者	ELSEVIER
WOS记录号	WOS:000709963300005
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/46279]
专题	自动化研究所_复杂系统管理与控制国家重点实验室_互联网大数据与安全信息学研究中心
通讯作者	Li, Linjing
作者单位	1.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing, Peoples R China 2.Shenzhen Artificial Intelligence & Data Sci Inst, Shenzhen, Peoples R China 3.Chinese Acad Sci, Inst Automat, Beijing, Peoples R China
推荐引用方式 GB/T 7714	Yan, Peng,Li, Linjing,Zeng, Daniel. Quantum Probability-inspired Graph Attention Network for Modeling Complex Text Interaction[J]. KNOWLEDGE-BASED SYSTEMS,2021,234:12.
APA	Yan, Peng,Li, Linjing,&Zeng, Daniel.(2021).Quantum Probability-inspired Graph Attention Network for Modeling Complex Text Interaction.KNOWLEDGE-BASED SYSTEMS,234,12.
MLA	Yan, Peng,et al."Quantum Probability-inspired Graph Attention Network for Modeling Complex Text Interaction".KNOWLEDGE-BASED SYSTEMS 234(2021):12.