A mutually enhanced multi-scale relation-aware graph convolutional network for argument pair extraction

doi:10.1007/s10844-023-00826-9

CSpace

	A mutually enhanced multi-scale relation-aware graph convolutional network for argument pair extraction
	Zhu, Xiaofei 1; Liu, Yidan 1; Chen, Zhuo 1; Chen, Xu 2; Guo, Jiafeng 3; Dietze, Stefan 4,5
	2023-11-30
发表期刊	JOURNAL OF INTELLIGENT INFORMATION SYSTEMS
ISSN	0925-9902
页码	20
摘要	Argument pair extraction (APE) is a fine-grained task of argument mining which aims to identify arguments offered by different participants in some discourse and detect interaction relationships between arguments from different participants. In recent years, many research efforts have been devoted to dealing with APE in a multi-task learning framework. Although these approaches have achieved encouraging results, they still face several challenging issues. First, different types of sentence relationships as well as different levels of information exchange among sentences are largely ignored. Second, they solely model interactions between argument pairs either in an explicit or implicit strategy, while neglecting the complementary effect of the two strategies. In this paper, we propose a novel Mutually Enhanced Multi-Scale Relation-Aware Graph Convolutional Network (MMR-GCN) for APE. Specifically, we first design a multi-scale relation-aware graph aggregation module to explicitly model the complex relationships between review and rebuttal passage sentences. In addition, we propose a mutually enhancement transformer module to implicitly and interactively enhance representations of review and rebuttal passage sentences. We experimentally validate MMR-GCN by comparing with the state-of-the-art APE methods. Experimental results show that it considerably outperforms all baseline methods, and the relative performance improvement of MMR-GCN over the best performing baseline MRC-APE in terms of F1 score reaches to 3.48% and 4.43% on the two benchmark datasets, respectively.
关键词	Argument mining Argument pair extraction Transformer Graph convolutional network
DOI	10.1007/s10844-023-00826-9
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China ; Natural Science Foundation of Chongqing, China[CSTB2022NSCQ-MSX1672] ; Major Project of Science and Technology Research Program of Chongqing Education Commission of China[KJZD-M202201102] ; Federal Ministry of Education and Research[01IS21086] ; [62141201]
WOS研究方向	Computer Science
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Information Systems
WOS记录号	WOS:001109135000001
出版者	SPRINGER
引用统计
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/38076
专题	中国科学院计算技术研究所
通讯作者	Chen, Xu
作者单位	1.Chongqing Univ Technol, Coll Comp Sci & Engn, Chongqing 400054, Peoples R China 2.Chongqing Univ Technol, Coll Accounting, Chongqing 400054, Peoples R China 3.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China 4.Leibniz Inst Social Sci, Knowledge Technol Social Sci, D-50667 Cologne, Germany 5.Heinrich Heine Univ Dusseldorf, Inst Comp Sci, D-40225 Dusseldorf, Germany
推荐引用方式 GB/T 7714	Zhu, Xiaofei,Liu, Yidan,Chen, Zhuo,et al. A mutually enhanced multi-scale relation-aware graph convolutional network for argument pair extraction[J]. JOURNAL OF INTELLIGENT INFORMATION SYSTEMS,2023:20.
APA	Zhu, Xiaofei,Liu, Yidan,Chen, Zhuo,Chen, Xu,Guo, Jiafeng,&Dietze, Stefan.(2023).A mutually enhanced multi-scale relation-aware graph convolutional network for argument pair extraction.JOURNAL OF INTELLIGENT INFORMATION SYSTEMS,20.
MLA	Zhu, Xiaofei,et al."A mutually enhanced multi-scale relation-aware graph convolutional network for argument pair extraction".JOURNAL OF INTELLIGENT INFORMATION SYSTEMS (2023):20.