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Inf. Syst."],"published-print":{"date-parts":[[2021,10,31]]},"abstract":"<jats:p>\n            Building an intelligent dialogue system with the ability to select a proper response according to a multi-turn context is challenging in three aspects: (1) the meaning of a context\u2013response pair is built upon language units from multiple granularities (e.g., words, phrases, and sub-sentences, etc.); (2) local (e.g., a small window around a word) and long-range (e.g., words across the context and the response) dependencies may exist in dialogue data; and (3) the relationship between the context and the response candidate lies in multiple relevant semantic clues or relatively implicit semantic clues in some real cases. However, existing approaches usually encode the dialogue with mono-type representation and the interaction processes between the context and the response candidate are executed in a rather shallow manner, which may lead to an inadequate understanding of dialogue content and hinder the recognition of the semantic relevance between the context and response. To tackle these challenges, we propose a representation\n            <jats:sup>\n              [\n              <jats:italic>K<\/jats:italic>\n              ]\n            <\/jats:sup>\n            -interaction\n            <jats:sup>\n              [\n              <jats:italic>L<\/jats:italic>\n              ]\n            <\/jats:sup>\n            -matching framework that explores multiple types of deep interactive representations to build context-response matching models for response selection. Particularly, we construct different types of representations for utterance\u2013response pairs and deepen them via alternate encoding and interaction. By this means, the model can handle the relation of neighboring elements, phrasal pattern, and long-range dependencies during the representation and make a more accurate prediction through multiple layers of interactions between the context\u2013response pair. Experiment results on three public benchmarks indicate that the proposed model significantly outperforms previous conventional context-response matching models and achieve slightly better results than the BERT model for multi-turn response selection in retrieval-based dialogue systems.\n          <\/jats:p>","DOI":"10.1145\/3462207","type":"journal-article","created":{"date-parts":[[2021,8,17]],"date-time":"2021-08-17T13:57:35Z","timestamp":1629208655000},"page":"1-28","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":8,"title":["Response Ranking with Multi-types of Deep Interactive Representations in Retrieval-based Dialogues"],"prefix":"10.1145","volume":"39","author":[{"given":"Ruijian","family":"Xu","sequence":"first","affiliation":[{"name":"Peking University, Haidian, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chongyang","family":"Tao","sequence":"additional","affiliation":[{"name":"Wangxuan Institute of Computer Technology, Peking University, Haidian, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiazhan","family":"Feng","sequence":"additional","affiliation":[{"name":"Wangxuan Institute of Computer Technology, Peking University, Haidian, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei","family":"Wu","sequence":"additional","affiliation":[{"name":"Microsoft Corporation, Haidian, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rui","family":"Yan","sequence":"additional","affiliation":[{"name":"Gaoling School of Artificial Intelligence, Renmin University of China, Haidian, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dongyan","family":"Zhao","sequence":"additional","affiliation":[{"name":"Wangxuan Institute of Computer Technology, Peking University, Haidian, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2021,8,17]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"Mart\u00edn Abadi Ashish Agarwal Paul Barham Eugene Brevdo Zhifeng Chen Craig Citro Greg S. 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