{"type":"ScholarlyArticle","title":"EmoElicitor: An Open Domain Response Generation Model with User Emotional Reaction Awareness","authors":[{"name":"Shifeng Li","slug":"shifeng-li","given":"Shifeng","family":"Li","affiliation":"Northeastern University , Shenyang , China"},{"name":"Shi Feng","slug":"shi-feng","given":"Shi","family":"Feng","affiliation":"Northeastern University , Shenyang , China"},{"name":"Daling Wang","slug":"daling-wang","given":"Daling","family":"Wang","affiliation":"Northeastern University , Shenyang , China"},{"name":"Kaisong Song","slug":"kaisong-song","given":"Kaisong","family":"Song","affiliation":"Alibaba Group , Hangzhou , China"},{"name":"Yifei Zhang","slug":"yifei-zhang","given":"Yifei","family":"Zhang","affiliation":"Northeastern University , Shenyang , China"},{"name":"Weichao Wang","slug":"weichao-wang","given":"Weichao","family":"Wang","affiliation":"Northeastern University , Shenyang , China"}],"abstract":"Generating emotional responses is crucial for building human-like dialogue systems. However, existing studies  have focused only on generating responses by controlling the agents' emotions, while the feelings of the users, which are the ultimate concern of a dialogue system, have been neglected. In this paper, we propose a novel variational model named EmoElicitor to generate appropriate responses that can elicit user's specific emotion. We incorporate the next-round utterance after the response into the posterior network to enrich the context, and we decompose single latent variable into several sequential ones to guide response generation with the help of a pre-trained language model. Extensive experiments conducted on real-world dataset show that EmoElicitor not only performs better than the baselines in term of diversity and semantic similarity, but also can elicit emotion with higher accuracy.","doi":"10.24963/ijcai.2020/503","url":"https://ijcai.org/proceedings/2020/503","pdf":"https://ijcai.org/proceedings/2020/0503.pdf","markdown":"https://ijcai.org/proceedings/2020/503.md","bibtex":"https://ijcai.org/proceedings/2020/bibtex/503","volume":{"slug":"2020","title":"Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2020","url":"https://ijcai.org/proceedings/2020/","year":2020,"location":"Yokohama","dates":"January 2021 (postponed from 11-17 July 2020)","editors":"Christian Bessiere","isbn":"978-0-9992411-6-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2020"},"section":"Natural Language Processing","track":"Main track","number":503,"pages":{"first":3637,"last":3643},"keywords":["Natural Language Processing: Dialogue","Natural Language Processing: Natural Language Generation"],"videos":[{"label":"Video","url":"https://videos.ijcai20.org/24508/file_24508.m3u8","length_sec":268,"provider":"hls"}],"references":[{"position":1,"raw":"[Chung et al., 2015] Junyoung Chung, Kyle Kastner, Lau- rent Dinh, Kratarth Goel, Aaron C. 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