Heterogeneous Community Question Answering via Social-Aware Multi-Modal Co-Attention Convolutional Matching

doi:10.1109/TMM.2020.3009491

	Heterogeneous Community Question Answering via Social-Aware Multi-Modal Co-Attention Convolutional Matching
	Hu, Jun 1; Qian, Shengsheng1,2 ; Fang, Quan1,2 ; Xu, Changsheng1,2,3
发表期刊	IEEE TRANSACTIONS ON MULTIMEDIA
ISSN	1520-9210
	2021
卷号	23 页码:2321-2334
通讯作者	Xu, Changsheng(csxu@nlpria.ac.cn)
摘要	Nowadays, community-based question answering (CQA) systems are popular and have accumulated a large number of questions and answers provided by users. How to accurately match relevant answers for a given question is an essential function in CQA tasks. Recent effective methods utilize word-pair interactions between questions and answers for CQA matching. However, these approaches usually encode questions and answers independently and ignore the fact that they can complement and enhance each other to provide better representations and thus more implicit interactions can be captured. In addition, the visual information, social information and the variable-length problem are usually ignored by most existing approaches. In this paper, a Social-aware Multi-modal Co-attention Convolutional Matching method (SMCACM) is proposed, which models the multi-modal content and social context of questions and answers in a unified framework for CQA matching. A novel co-attention network is proposed to extract complementary information from questions and answers to enhance each other for obtaining better representations, through which our model can capture more implicit interactions between questions and answers. In addition to textual content, our model uses object detection techniques and a meta-path based heterogeneous social representation learning approach to take advantage of the visual content and social context in CQA systems, respectively. Finally, a pooling-based convolutional matching network is designed to infer the matching score based on the complemented questions and answers, which can accept variable-length answers as inputs without padding or cutting. Experimental results on two real-world datasets demonstrate the superior performance of SMCACM compared with other state-of-the-art algorithms.
关键词	Visualization Semantics Knowledge discovery Context modeling Portable computers Task analysis Object detection Question-answering attention multi-modal social multimedia
DOI	10.1109/TMM.2020.3009491
关键词[WOS]	RECOMMENDATION
收录类别	SCI
语种	英语
资助项目	National Key Research and Development Program of China[2017YFB1002804] ; National Natural Science Foundation of China[61720106006] ; National Natural Science Foundation of China[61572503] ; National Natural Science Foundation of China[61802405] ; National Natural Science Foundation of China[61872424] ; National Natural Science Foundation of China[61702509] ; National Natural Science Foundation of China[61832002] ; National Natural Science Foundation of China[61936005] ; National Natural Science Foundation of China[U1705262] ; Key Research Program of Frontier Sciences, CAS[QYZDJ-SSW-JSC039] ; K. C. Wong Education Foundation
项目资助者	National Key Research and Development Program of China ; National Natural Science Foundation of China ; Key Research Program of Frontier Sciences, CAS ; K. C. Wong Education Foundation
WOS研究方向	Computer Science ; Telecommunications
WOS类目	Computer Science, Information Systems ; Computer Science, Software Engineering ; Telecommunications
WOS记录号	WOS:000679533800013
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
七大方向——子方向分类	多模态智能
引用统计	被引频次：5[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/45576
专题	多模态人工智能系统全国重点实验室_多媒体计算
通讯作者	Xu, Changsheng
作者单位	1.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China 3.Peng Cheng Lab, Shenzhen 518066, Peoples R China
第一作者单位	模式识别国家重点实验室
通讯作者单位	模式识别国家重点实验室
推荐引用方式 GB/T 7714	Hu, Jun,Qian, Shengsheng,Fang, Quan,et al. Heterogeneous Community Question Answering via Social-Aware Multi-Modal Co-Attention Convolutional Matching[J]. IEEE TRANSACTIONS ON MULTIMEDIA,2021,23:2321-2334.
APA	Hu, Jun,Qian, Shengsheng,Fang, Quan,&Xu, Changsheng.(2021).Heterogeneous Community Question Answering via Social-Aware Multi-Modal Co-Attention Convolutional Matching.IEEE TRANSACTIONS ON MULTIMEDIA,23,2321-2334.
MLA	Hu, Jun,et al."Heterogeneous Community Question Answering via Social-Aware Multi-Modal Co-Attention Convolutional Matching".IEEE TRANSACTIONS ON MULTIMEDIA 23(2021):2321-2334.