{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,10]],"date-time":"2026-01-10T03:06:21Z","timestamp":1768014381131,"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 state modules are a useful component in a task-oriented dialogue system. Traditional methods find dialogue states by manually labeling training corpora, upon which neural models are trained. However, the labeling process can be costly, slow, error-prone, and more importantly, cannot cover the vast range of domains in real-world dialogues for customer service. We propose the task of dialogue state induction, building two neural latent variable models that mine dialogue states automatically from unlabeled customer service dialogue records. Results show that the models can effectively find meaningful dialogue states. In addition, equipped with induced dialogue states, a state-of-the-art dialogue system gives better performance compared with not using a dialogue state module.<\/jats:p>","DOI":"10.24963\/ijcai.2020\/532","type":"proceedings-article","created":{"date-parts":[[2020,7,8]],"date-time":"2020-07-08T12:12:10Z","timestamp":1594210330000},"page":"3845-3852","source":"Crossref","is-referenced-by-count":6,"title":["Dialogue State Induction Using Neural Latent Variable Models"],"prefix":"10.24963","author":[{"given":"Qingkai","family":"Min","sequence":"first","affiliation":[{"name":"School of Engineering, Westlake University"},{"name":"Institute of Advanced Technology, Westlake Institute for Advanced Study"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Libo","family":"Qin","sequence":"additional","affiliation":[{"name":"Research Center for Social Computing and Information Retrieval, Harbin Institute of Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhiyang","family":"Teng","sequence":"additional","affiliation":[{"name":"School of Engineering, Westlake University"},{"name":"Institute of Advanced Technology, Westlake Institute for Advanced Study"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiao","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Beijing Institute of Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yue","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Engineering, Westlake University"},{"name":"Institute of Advanced Technology, Westlake Institute for Advanced Study"}],"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":[[2020,7,9]],"date-time":"2020-07-09T02:15:47Z","timestamp":1594260947000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2020\/532"}},"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\/532","relation":{},"subject":[],"published":{"date-parts":[[2020,7]]}}}