{"type":"ScholarlyArticle","title":"TopicKA: Generating Commonsense Knowledge-Aware Dialogue Responses Towards the Recommended Topic Fact","authors":[{"name":"Sixing Wu","slug":"sixing-wu","given":"Sixing","family":"Wu","affiliation":"School of Electronics Engineering and Computer Science , Peking University , Beijing , China"},{"name":"Ying Li","slug":"ying-li","given":"Ying","family":"Li","affiliation":"National Research Center of Software Engineering , Peking University , Beijing , China"},{"name":"Dawei Zhang","slug":"dawei-zhang","given":"Dawei","family":"Zhang","affiliation":"School of Electronics Engineering and Computer Science , Peking University , Beijing , China"},{"name":"Yang Zhou","slug":"yang-zhou","given":"Yang","family":"Zhou","affiliation":"Auburn University , Auburn , Alabama , USA"},{"name":"Zhonghai Wu","slug":"zhonghai-wu","given":"Zhonghai","family":"Wu","affiliation":"National Research Center of Software Engineering , Peking University , Beijing , China"}],"abstract":"Insufficient semantic understanding of dialogue always leads to the appearance of generic responses, in generative dialogue systems. Recently, high-quality knowledge bases have been introduced to enhance dialogue understanding, as well as to reduce the prevalence of boring responses. Although such knowledge-aware approaches have shown tremendous potential, they always utilize the knowledge in a black-box fashion. As a result, the generation process is somewhat uncontrollable, and it is also not interpretable. In this paper, we introduce a topic fact-based commonsense knowledge-aware approach, TopicKA. Different from previous works, TopicKA generates responses conditioned not only on the query message but also on a topic fact with an explicit semantic meaning, which also controls the direction of generation. Topic facts are recommended by a recommendation network trained under the Teacher-Student framework. To integrate the recommendation network and the generation network, this paper designs four schemes, which include two non-sampling schemes and two sampling methods. We collected and constructed a large-scale Chinese commonsense knowledge graph. Experimental results on an open Chinese benchmark dataset indicate that our model outperforms baselines in terms of both the objective and the subjective metrics.","doi":"10.24963/ijcai.2020/521","url":"https://ijcai.org/proceedings/2020/521","pdf":"https://ijcai.org/proceedings/2020/0521.pdf","markdown":"https://ijcai.org/proceedings/2020/521.md","bibtex":"https://ijcai.org/proceedings/2020/bibtex/521","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":521,"pages":{"first":3766,"last":3772},"keywords":["Natural Language Processing: Natural Language Generation","Natural Language Processing: Dialogue"],"videos":[{"label":"Video","url":"https://videos.ijcai20.org/24459/file_24459.m3u8","length_sec":277,"provider":"hls"}],"references":[{"position":1,"raw":"[Arevalo et al., 2017] John Arevalo, Thamar Solorio, Manuel Montes y Gómez, and Fabio A. 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