{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,28]],"date-time":"2026-01-28T00:10:16Z","timestamp":1769559016901,"version":"3.49.0"},"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":[[2020,7]]},"abstract":"<jats:p>Dialogue disentanglement aims to separate intermingled messages into detached sessions. The existing research focuses on two-step architectures, in which a model first retrieves the relationships between two messages and then divides the message stream into separate clusters. Almost all existing work puts significant efforts on selecting features for message-pair classification and clustering, while ignoring the semantic coherence within each session. In this paper, we introduce the first end-to- end transition-based model for online dialogue disentanglement. Our model captures the sequential information of each session as the online algorithm proceeds on processing a dialogue. The coherence in a session is hence modeled when messages are sequentially added into their best-matching sessions. Meanwhile, the research field still lacks data for studying end-to-end dialogue disentanglement, so we construct a large-scale dataset by extracting coherent dialogues from online movie scripts. We evaluate our model on both the dataset we developed and the publicly available Ubuntu IRC dataset [Kummerfeld et al., 2019]. The results show that our model significantly outperforms the existing algorithms. Further experiments demonstrate that our model better captures the sequential semantics and obtains more coherent disentangled sessions.<\/jats:p>","DOI":"10.24963\/ijcai.2020\/535","type":"proceedings-article","created":{"date-parts":[[2020,7,8]],"date-time":"2020-07-08T08:12:10Z","timestamp":1594195930000},"page":"3868-3874","source":"Crossref","is-referenced-by-count":14,"title":["End-to-End Transition-Based Online Dialogue Disentanglement"],"prefix":"10.24963","author":[{"given":"Hui","family":"Liu","sequence":"first","affiliation":[{"name":"Ingenuity Labs Research Institute & ECE, Queen\u2019s University, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhan","family":"Shi","sequence":"additional","affiliation":[{"name":"Ingenuity Labs Research Institute & ECE, Queen\u2019s University, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jia-Chen","family":"Gu","sequence":"additional","affiliation":[{"name":"University of Science and Technology of China, Hefei, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Quan","family":"Liu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Cognitive Intelligence, iFLYTEK Research, Hefei, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Si","family":"Wei","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Cognitive Intelligence, iFLYTEK Research, Hefei, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaodan","family":"Zhu","sequence":"additional","affiliation":[{"name":"Ingenuity Labs Research Institute & ECE, Queen\u2019s University, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"10584","event":{"name":"Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}","theme":"Artificial Intelligence","location":"Yokohama, Japan","acronym":"IJCAI-PRICAI-2020","number":"28","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"start":{"date-parts":[[2020,7,11]]},"end":{"date-parts":[[2020,7,17]]}},"container-title":["Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2020,7,8]],"date-time":"2020-07-08T22:15:48Z","timestamp":1594246548000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2020\/535"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2020,7]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2020\/535","relation":{},"subject":[],"published":{"date-parts":[[2020,7]]}}}