{"type":"ScholarlyArticle","title":"End-to-End Transition-Based Online Dialogue Disentanglement","authors":[{"name":"Hui Liu","slug":"hui-liu","given":"Hui","family":"Liu","affiliation":"Ingenuity Labs Research Institute & ECE , Queen's University , Canada"},{"name":"Zhan Shi","slug":"zhan-shi","given":"Zhan","family":"Shi","affiliation":"Ingenuity Labs Research Institute & ECE , Queen's University , Canada"},{"name":"Jia-Chen Gu","slug":"jia-chen-gu","given":"Jia-Chen","family":"Gu","affiliation":"University of Science and Technology of China , Hefei , China"},{"name":"Quan Liu","slug":"quan-liu","given":"Quan","family":"Liu","affiliation":"State Key Laboratory of Cognitive Intelligence , iFLYTEK Research , Hefei , China"},{"name":"Si Wei","slug":"si-wei","given":"Si","family":"Wei","affiliation":"State Key Laboratory of Cognitive Intelligence , iFLYTEK Research , Hefei , China"},{"name":"Xiaodan Zhu","slug":"xiaodan-zhu","given":"Xiaodan","family":"Zhu","affiliation":"Ingenuity Labs Research Institute & ECE , Queen's University , Canada"}],"abstract":"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.","doi":"10.24963/ijcai.2020/535","url":"https://ijcai.org/proceedings/2020/535","pdf":"https://ijcai.org/proceedings/2020/0535.pdf","markdown":"https://ijcai.org/proceedings/2020/535.md","bibtex":"https://ijcai.org/proceedings/2020/bibtex/535","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":535,"pages":{"first":3868,"last":3874},"keywords":["Natural Language Processing: Dialogue","Natural Language Processing: Information Retrieval","Natural Language Processing: Information Extraction"],"videos":[{"label":"Video","url":"https://videos.ijcai20.org/26834/file_26834.m3u8","length_sec":711,"provider":"hls"}],"references":[{"position":1,"raw":"[Allan, 2012] James Allan. 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