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

文献类型：期刊论文

作者Ma, Xuan1,2,3; Yang, Xiaoshan1,2,3; Xu, Changsheng1,2,3
刊名ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS
出版日期2023-07-01
卷号19期号:4页码:17
ISSN号1551-6857
关键词Knowledge reasoning multi-modal commonsense inference graph neural network
DOI10.1145/3573201
通讯作者Ma, Xuan(maxuan2018@ia.ac.cn)
英文摘要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.
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]
WOS研究方向Computer Science
语种英语
出版者ASSOC COMPUTING MACHINERY
WOS记录号WOS:001011937600003
资助机构National Key Research and Development Program of China ; National Natural Science Foundation of China ; Beijing Natural Science Foundation
源URL[http://ir.ia.ac.cn/handle/173211/53711]  
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.Peng Cheng Lab, Shenzhen, Peoples R China
2.Univ Chinese Acad Sci, Inst Automat, Chinese Acad Sci, Natl Lab Pattern Recognit, 95 Zhongguancun East Rd, Beijing, Peoples R China
3.Univ Chinese Acad Sci, Sch Artificial Intelligence, 95 Zhongguancun East Rd, Beijing, Peoples R China
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.

入库方式： OAI收割

来源：自动化研究所

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