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

文献类型：期刊论文

作者Hu, Jun1; Qian, Shengsheng1,2; Fang, Quan1,2; Xu, Changsheng1,2,3
刊名IEEE TRANSACTIONS ON MULTIMEDIA
出版日期2021
卷号23页码:2321-2334
关键词Visualization Semantics Knowledge discovery Context modeling Portable computers Task analysis Object detection Question-answering attention multi-modal social multimedia
ISSN号1520-9210
DOI10.1109/TMM.2020.3009491
通讯作者Xu, Changsheng(csxu@nlpria.ac.cn)
英文摘要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.
WOS关键词RECOMMENDATION
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
WOS研究方向Computer Science ; Telecommunications
语种英语
WOS记录号WOS:000679533800013
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
资助机构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
源URL[http://ir.ia.ac.cn/handle/173211/45575]  
专题自动化研究所_模式识别国家重点实验室_多媒体计算与图形学团队
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
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.

入库方式： OAI收割

来源：自动化研究所

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