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Densely Enhanced Semantic Network for Conversation System in Social Media

文献类型：期刊论文

作者Li, Yongrui1,3; Wang, Zengfu1,2,3; Yu, Jun1,3
刊名ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS
出版日期2022-11-01
卷号18
关键词Social media question answering multimedia application deep learning
ISSN号1551-6857
DOI10.1145/3501799
通讯作者Wang, Zengfu(zfwang@ustc.edu.cn) ; Yu, Jun(harryjun@ustc.edu.cn)
英文摘要The human-computer conversation system is a significant application in the field of multimedia. To select an appropriate response, retrieval-based systems model the matching between the dialogue history and response candidates. However, most of the existing methods cannot fully capture and utilize varied matching patterns, which may degrade the performance of the systems. To address the issue, a densely enhanced semantic network (DESN) is proposed in our work. Given a multi-turn dialogue history and a response candidate, DESN first constructs the semantic representations of sentences from the word perspective, the sentence perspective, and the dialogue perspective. In particular, the dialogue perspective is a novel one introduced in our work. The dependencies between a single sentence and the whole dialogue are modeled from the dialogue perspective. Then, the response candidate and each utterance in the dialogue history are made to interact with each other. The varied matching patterns are captured for each utterance-response pair by using a dense matching module. The matching patterns of all the utterance-response pairs are accumulated in chronological order to calculate the matching degree between the dialogue history and the response. The responses in the candidate pool are ranked with the matching degree, thereby returning the most appropriate candidate. Our model is evaluated on the benchmark datasets. The experimental results prove that our model achieves significant and consistent improvement when compared with other baselines.
资助项目Strategic Priority Research Program of the Chinese Academy of Sciences[XDC08020400]
WOS研究方向Computer Science
语种英语
WOS记录号WOS:000776441600012
出版者ASSOC COMPUTING MACHINERY
资助机构Strategic Priority Research Program of the Chinese Academy of Sciences
源URL[http://ir.hfcas.ac.cn:8080/handle/334002/128247]  
专题中国科学院合肥物质科学研究院
通讯作者Wang, Zengfu; Yu, Jun
作者单位1.Univ Sci & Technol China, 96 JinZhai Rd, Hefei 230026, Anhui, Peoples R China
2.Chinese Acad Sci, Inst Intelligent Machines, Beijing, Peoples R China
3.Univ Sci & Technol China, Sch Informat Sci & Technol, Hefei, Peoples R China
推荐引用方式
GB/T 7714		 Li, Yongrui,Wang, Zengfu,Yu, Jun. Densely Enhanced Semantic Network for Conversation System in Social Media[J]. ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS,2022,18.
APA Li, Yongrui,Wang, Zengfu,&Yu, Jun.(2022).Densely Enhanced Semantic Network for Conversation System in Social Media.ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS,18.
MLA Li, Yongrui,et al."Densely Enhanced Semantic Network for Conversation System in Social Media".ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS 18(2022).

入库方式： OAI收割

来源：合肥物质科学研究院

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