{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T17:06:27Z","timestamp":1791911187547,"version":"4.3.4"},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,8]]},"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.24963\/ijcai.2019\/696","type":"proceedings-article","created":{"date-parts":[[2019,7,28]],"date-time":"2019-07-28T07:46:05Z","timestamp":1564299965000},"page":"5010-5016","source":"Crossref","is-referenced-by-count":27,"title":["GSN: A Graph-Structured Network for Multi-Party Dialogues"],"prefix":"10.24963","author":[{"given":"Wenpeng","family":"Hu","sequence":"first","affiliation":[{"name":"Department of Information Science, School of Mathematical Sciences, Peking University"},{"name":"Institute of Computer Science and Technology, Peking University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhangming","family":"Chan","sequence":"additional","affiliation":[{"name":"Center for Data Science, Peking University"},{"name":"Institute of Computer Science and Technology, Peking University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bing","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Illinois at Chicago"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dongyan","family":"Zhao","sequence":"additional","affiliation":[{"name":"Center for Data Science, Peking University"},{"name":"Institute of Computer Science and Technology, Peking University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinwen","family":"Ma","sequence":"additional","affiliation":[{"name":"Department of Information Science, School of Mathematical Sciences, Peking University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rui","family":"Yan","sequence":"additional","affiliation":[{"name":"Center for Data Science, Peking University"},{"name":"Institute of Computer Science and Technology, Peking University"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"10584","event":{"name":"Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}","theme":"Artificial Intelligence","location":"Macao, China","acronym":"IJCAI-2019","number":"28","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"start":{"date-parts":[[2019,8,10]]},"end":{"date-parts":[[2019,8,16]]}},"container-title":["Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2019,7,28]],"date-time":"2019-07-28T07:51:08Z","timestamp":1564300268000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2019\/696"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2019,8]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2019\/696","relation":{},"subject":[],"published":{"date-parts":[[2019,8]]}}}