{"type":"ScholarlyArticle","title":"Enhancing Dialog Coherence with Event Graph Grounded Content Planning","authors":[{"name":"Jun Xu","slug":"jun-xu","given":"Jun","family":"Xu","affiliation":"Research Center for Social Computing and Information Retrieval , Harbin Institute of Technology , Harbin , China"},{"name":"Zeyang Lei","slug":"zeyang-lei","given":"Zeyang","family":"Lei","affiliation":"Baidu Inc. , Beijing , China"},{"name":"Haifeng Wang","slug":"haifeng-wang","given":"Haifeng","family":"Wang","affiliation":"Baidu Inc. , Beijing , China"},{"name":"Zheng-Yu Niu","slug":"zheng-yu-niu","given":"Zheng-Yu","family":"Niu","affiliation":"Baidu Inc. , Beijing , China"},{"name":"Hua Wu","slug":"hua-wu","given":"Hua","family":"Wu","affiliation":"Baidu Inc. , Beijing , China"},{"name":"Wanxiang Che","slug":"wanxiang-che","given":"Wanxiang","family":"Che","affiliation":"Research Center for Social Computing and Information Retrieval , Harbin Institute of Technology , Harbin , China"}],"abstract":"How to generate informative, coherent and sustainable open-domain conversations is a non-trivial task. Previous work on knowledge grounded conversation generation focus on improving dialog informativeness with little attention on dialog coherence. In this paper, to enhance multi-turn dialog coherence, we propose to leverage event chains to help determine a sketch of a multi-turn dialog. We first extract event chains from narrative texts and connect them as a graph. We then present a novel event graph grounded Reinforcement Learning (RL) framework. It conducts high-level response content (simply an event) planning by learning to walk over the graph, and then produces a response conditioned on the planned content. In particular, we devise a novel multi-policy decision making mechanism to foster a coherent dialog with both appropriate content ordering and high contextual relevance. Experimental results indicate the effectiveness of this framework in terms of dialog coherence and informativeness.","doi":"10.24963/ijcai.2020/545","url":"https://ijcai.org/proceedings/2020/545","pdf":"https://ijcai.org/proceedings/2020/0545.pdf","markdown":"https://ijcai.org/proceedings/2020/545.md","bibtex":"https://ijcai.org/proceedings/2020/bibtex/545","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":545,"pages":{"first":3941,"last":3947},"keywords":["Natural Language Processing: Dialogue"],"videos":[{"label":"Video","url":"https://videos.ijcai20.org/25460/file_25460.m3u8","length_sec":276,"provider":"hls"}],"references":[{"position":1,"raw":"[Bao et al., 2019] Siqi Bao, Huang He, Fan Wang, Rongzhong Lian, and Hua Wu. 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