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Dual-factor Generation Model for Conversation

文献类型：期刊论文

作者Zhang, Ruqing1,2; Guo, Jiafeng1,2; Fan, Yixing1,2; Lan, Yanyan1,2; Cheng, Xueqi1,2
刊名ACM TRANSACTIONS ON INFORMATION SYSTEMS
出版日期2020-06-01
卷号38期号:3页码:31
关键词Conversation dual-factor generation responder state modeling
ISSN号1046-8188
DOI10.1145/3394052
英文摘要The conversation task is usually formulated as a conditional generation problem, i.e., to generate a natural and meaningful response given the input utterance. Generally speaking, this formulation is apparently based on an oversimplified assumption that the response is solely dependent on the input utterance. It ignores the subjective factor of the responder, e.g., his/her emotion or knowledge state, which is a major factor that affects the response in practice. Without explicitly differentiating such subjective factor behind the response, existing generation models can only learn the general shape of conversations, leading to the blandness problem of the response. Moreover, there is no intervention mechanism within the existing generation process, since the response is fully decided by the input utterance. In this work, we propose to view the conversation task as a dual-factor generation problem, including an objective factor denoting the input utterance and a subjective factor denoting the responder state. We extend the existing neural sequence-to-sequence (Seq2Seq) model to accommodate the responder state modeling. We introduce two types of responder state, i.e., discrete and continuous state, to model emotion state and topic preference state, respectively. We show that with our dual-factor generation model, we can not only better fit the conversation data, but also actively control the generation of the response with respect to sentiment or topic specificity.
资助项目Beijing Academy of Artificial Intelligence (BAAI)[BAAI2019ZD0306] ; National Natural Science Foundation of China (NSFC)[61722211] ; National Natural Science Foundation of China (NSFC)[61773362] ; National Natural Science Foundation of China (NSFC)[61872338] ; National Natural Science Foundation of China (NSFC)[61902381] ; Youth Innovation Promotion Association CAS[20144310] ; Youth Innovation Promotion Association CAS[2016102] ; National Key RD Program of China[2016QY02D0405] ; Lenovo-CAS Joint Lab Youth Scientist Project ; Foundation and Frontier Research Key Program of Chongqing Science and Technology Commission[cstc2017jcyjBX0059]
WOS研究方向Computer Science
语种英语
WOS记录号WOS:000583695800011
出版者ASSOC COMPUTING MACHINERY
源URL[http://119.78.100.204/handle/2XEOYT63/16092]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Guo, Jiafeng
作者单位1.Univ Chinese Acad Sci, Beijing, Peoples R China
2.Chinese Acad Sci, Inst Comp Technol, CAS Key Lab Network Data Sci & Technol, 6 Kexueyuan South Rd, Beijing 100190, Peoples R China
推荐引用方式
GB/T 7714		 Zhang, Ruqing,Guo, Jiafeng,Fan, Yixing,et al. Dual-factor Generation Model for Conversation[J]. ACM TRANSACTIONS ON INFORMATION SYSTEMS,2020,38(3):31.
APA Zhang, Ruqing,Guo, Jiafeng,Fan, Yixing,Lan, Yanyan,&Cheng, Xueqi.(2020).Dual-factor Generation Model for Conversation.ACM TRANSACTIONS ON INFORMATION SYSTEMS,38(3),31.
MLA Zhang, Ruqing,et al."Dual-factor Generation Model for Conversation".ACM TRANSACTIONS ON INFORMATION SYSTEMS 38.3(2020):31.

入库方式： OAI收割

来源：计算技术研究所

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