{"type":"ScholarlyArticle","title":"GSN: A Graph-Structured Network for Multi-Party Dialogues","authors":[{"name":"Wenpeng Hu","slug":"wenpeng-hu","given":"Wenpeng","family":"Hu","affiliation":"Department of Information Science , School of Mathematical Sciences , Peking University; Institute of Computer Science and Technology , Peking University"},{"name":"Zhangming Chan","slug":"zhangming-chan","given":"Zhangming","family":"Chan","affiliation":"Center for Data Science , Peking University; Institute of Computer Science and Technology , Peking University"},{"name":"Bing Liu","slug":"bing-liu","given":"Bing","family":"Liu","affiliation":"Department of Computer Science , University of Illinois at Chicago"},{"name":"Dongyan Zhao","slug":"dongyan-zhao","given":"Dongyan","family":"Zhao","affiliation":"Center for Data Science , Peking University; Institute of Computer Science and Technology , Peking University"},{"name":"Jinwen Ma","slug":"jinwen-ma","given":"Jinwen","family":"Ma","affiliation":"Department of Information Science , School of Mathematical Sciences , Peking University"},{"name":"Rui Yan","slug":"rui-yan","given":"Rui","family":"Yan","affiliation":"Center for Data Science , Peking University; Institute of Computer Science and Technology , Peking University"}],"abstract":"Existing neural models for dialogue response generation assume that utterances are sequentially organized. However, many real-world dialogues involve multiple interlocutors (i.e., multi-party dialogues), where the assumption does not hold as utterances from different interlocutors can occur ``in parallel.'' This paper generalizes existing sequence-based models to a Graph-Structured neural Network (GSN) for dialogue modeling. The core of GSN is a graph-based encoder that can model the information flow along the graph-structured dialogues (two-party sequential dialogues are a special case). Experimental results show that GSN significantly outperforms existing sequence-based models.","doi":"10.24963/ijcai.2019/696","url":"https://ijcai.org/proceedings/2019/696","pdf":"https://ijcai.org/proceedings/2019/0696.pdf","markdown":"https://ijcai.org/proceedings/2019/696.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/696","volume":{"slug":"2019","title":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2019","url":"https://ijcai.org/proceedings/2019/","year":2019,"location":"Macao","dates":"10-16 August 2019","editors":"Sarit Kraus","isbn":"978-0-9992411-4-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2019"},"section":"Natural Language Processing","track":"Main track","number":696,"pages":{"first":5010,"last":5016},"keywords":["Natural Language Processing: Dialogue","Natural Language Processing: Natural Language Generation"],"videos":[],"references":[{"position":1,"raw":"[Bahdanau et al., 2014] Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 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