Multi-Source Knowledge Reasoning Graph Network for Multi-Modal Commonsense Inference

doi:10.1145/3573201

	Multi-Source Knowledge Reasoning Graph Network for Multi-Modal Commonsense Inference
	Ma, Xuan1,2,3 ; Yang, Xiaoshan1,2,3 ; Xu, Changsheng1,2,3
发表期刊	ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS
ISSN	1551-6857
	2023-07-01
卷号	19 期号:4 页码:17
通讯作者	Ma, Xuan(maxuan2018@ia.ac.cn)
摘要	As a crucial part of natural language processing, event-centered commonsense inference task has attracted increasing attention. With a given observed event, the intention and reaction of the people involved in the event are required to be inferred with artificial intelligent algorithms. To solve this problem, sequence-to-sequence methods are widely studied, where the event is first encoded into a specific representation and then decoded to generate the results. However, all the existing methods learn the event representation only with the textual information, while the visual information is ignored, which is actually helpful for the commonsense reference. In this article, we first define a new task of multi-modal commonsense reference with both textual and visual information. A new event-centered multi-modal dataset is also provided. Then we propose a multi-source knowledge reasoning graph network to solve this task, where three kinds of relational knowledge are considered. Multi-modal correlations are learned to get the event's multi-modal representation from a global perspective. Intra-event object relations are explored to capture the fine-grained event feature with an object graph. Inter-event semantic relations are also explored through the external knowledge to understand the semantic associations among events with an event graph. We conduct extensive experiments on the new dataset, and the results show the effectiveness of our method.
关键词	Knowledge reasoning multi-modal commonsense inference graph neural network
DOI	10.1145/3573201
收录类别	SCI
语种	英语
资助项目	National Key Research and Development Program of China[2018AAA0100604] ; National Natural Science Foundation of China[61720106006] ; National Natural Science Foundation of China[62036012] ; National Natural Science Foundation of China[62072455] ; National Natural Science Foundation of China[61721004] ; National Natural Science Foundation of China[U1836220] ; National Natural Science Foundation of China[61872424] ; Beijing Natural Science Foundation[L201001]
项目资助者	National Key Research and Development Program of China ; National Natural Science Foundation of China ; Beijing Natural Science Foundation
WOS研究方向	Computer Science
WOS类目	Computer Science, Information Systems ; Computer Science, Software Engineering ; Computer Science, Theory & Methods
WOS记录号	WOS:001011937600003
出版者	ASSOC COMPUTING MACHINERY
引用统计
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/53712
专题	多模态人工智能系统全国重点实验室
通讯作者	Ma, Xuan
作者单位	1.Univ Chinese Acad Sci, Inst Automat, Chinese Acad Sci, Natl Lab Pattern Recognit, 95 Zhongguancun East Rd, Beijing, Peoples R China 2.Univ Chinese Acad Sci, Sch Artificial Intelligence, 95 Zhongguancun East Rd, Beijing, Peoples R China 3.Peng Cheng Lab, Shenzhen, 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	Ma, Xuan,Yang, Xiaoshan,Xu, Changsheng. Multi-Source Knowledge Reasoning Graph Network for Multi-Modal Commonsense Inference[J]. ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS,2023,19(4):17.
APA	Ma, Xuan,Yang, Xiaoshan,&Xu, Changsheng.(2023).Multi-Source Knowledge Reasoning Graph Network for Multi-Modal Commonsense Inference.ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS,19(4),17.
MLA	Ma, Xuan,et al."Multi-Source Knowledge Reasoning Graph Network for Multi-Modal Commonsense Inference".ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS 19.4(2023):17.