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Syst."],"published-print":{"date-parts":[[2021,10,31]]},"abstract":"<jats:p>Existing multi-turn context-response matching methods mainly concentrate on obtaining multi-level and multi-dimension representations and better interactions between context utterances and response. However, in real-place conversation scenarios, whether a response candidate is suitable not only counts on the given dialogue context but also other backgrounds, e.g., wording habits, user-specific dialogue history content. To fill the gap between these up-to-date methods and the real-world applications, we incorporate user-specific dialogue history into the response selection and propose a personalized hybrid matching network (PHMN). Our contributions are two-fold: (1) our model extracts personalized wording behaviors from user-specific dialogue history as extra matching information; (2) we perform hybrid representation learning on context-response utterances and explicitly incorporate a customized attention mechanism to extract vital information from context-response interactions so as to improve the accuracy of matching. We evaluate our model on two large datasets with user identification, i.e., personalized Ubuntu dialogue Corpus (P-Ubuntu) and personalized Weibo dataset (P-Weibo). Experimental results confirm that our method significantly outperforms several strong models by combining personalized attention, wording behaviors, and hybrid representation learning.<\/jats:p>","DOI":"10.1145\/3453183","type":"journal-article","created":{"date-parts":[[2021,8,17]],"date-time":"2021-08-17T13:57:35Z","timestamp":1629208655000},"page":"1-25","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":20,"title":["Dialogue History Matters! Personalized Response Selection in Multi-Turn Retrieval-Based Chatbots"],"prefix":"10.1145","volume":"39","author":[{"given":"Juntao","family":"Li","sequence":"first","affiliation":[{"name":"Wangxuan Institute of Computer Technology and Center for Data Science, Academy for Advanced Interdisciplinary Studies, Peking University, Haidian Qu, Beijing Shi, China"}]},{"given":"Chang","family":"Liu","sequence":"additional","affiliation":[{"name":"Wangxuan Institute of Computer Technology and Center for Data Science, Academy for Advanced Interdisciplinary Studies, Peking University, Haidian Qu, Beijing Shi, China"}]},{"given":"Chongyang","family":"Tao","sequence":"additional","affiliation":[{"name":"Wangxuan Institute of Computer Technology, Peking University, Haidian Qu, Beijing Shi, China"}]},{"given":"Zhangming","family":"Chan","sequence":"additional","affiliation":[{"name":"Wangxuan Institute of Computer Technology, Peking University, Haidian Qu, Beijing Shi, China"}]},{"given":"Dongyan","family":"Zhao","sequence":"additional","affiliation":[{"name":"Wangxuan Institute of Computer Technology, Peking University, Haidian Qu, Beijing Shi, China"}]},{"given":"Min","family":"Zhang","sequence":"additional","affiliation":[{"name":"Soochow University, Suzhou, Jiang Su, China"}]},{"given":"Rui","family":"Yan","sequence":"additional","affiliation":[{"name":"Gaoling School of Artificial Intelligence, Renmin University of China, Haidian Qu, Beijing Shi, China"}]}],"member":"320","published-online":{"date-parts":[[2021,8,17]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/3331184.3331265"},{"key":"e_1_2_1_2_1","volume-title":"Proceedings of the 3rd International Conference on Learning Representations (ICLR","author":"Bahdanau Dzmitry","year":"2015","unstructured":"Dzmitry Bahdanau , Kyunghyun Cho , and Yoshua Bengio . 2015 . 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