{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,25]],"date-time":"2026-01-25T03:54:48Z","timestamp":1769313288074,"version":"3.49.0"},"reference-count":23,"publisher":"Association for Computing Machinery (ACM)","issue":"6","license":[{"start":{"date-parts":[[2021,9,20]],"date-time":"2021-09-20T00:00:00Z","timestamp":1632096000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"SERB Women in Excellence Award 2018"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Asian Low-Resour. Lang. Inf. Process."],"published-print":{"date-parts":[[2021,11,30]]},"abstract":"<jats:p>\n            Building Virtual Agents capable of carrying out complex queries of the user involving multiple intents of a domain is quite a challenge, because it demands that the agent manages several subtasks simultaneously. This article presents a universal Deep Reinforcement Learning framework that can synthesize dialogue managers capable of working in a task-oriented dialogue system encompassing various intents pertaining to a domain. The conversation between agent and user is broken down into hierarchies, to segregate subtasks pertinent to different intents. The concept of Hierarchical Reinforcement Learning, particularly\n            <jats:italic>options<\/jats:italic>\n            , is used to learn policies in different hierarchies that operates in distinct time steps to fulfill the user query successfully. The dialogue manager comprises top-level intent meta-policy to select among subtasks or options and a low-level controller policy to pick primitive actions to communicate with the user to complete the subtask provided to it by the top-level policy in varying intents of a domain. The proposed dialogue management module has been trained in a way such that it can be reused for any language for which it has been developed with little to no supervision. The developed system has been demonstrated for \u201cAir Travel\u201d and \u201cRestaurant\u201d domain in\n            <jats:italic>English<\/jats:italic>\n            and\n            <jats:italic>Hindi<\/jats:italic>\n            languages. Empirical results determine the robustness and efficacy of the learned dialogue policy as it outperforms several baselines and a state-of-the-art system.\n          <\/jats:p>","DOI":"10.1145\/3461763","type":"journal-article","created":{"date-parts":[[2021,9,20]],"date-time":"2021-09-20T18:22:56Z","timestamp":1632162176000},"page":"1-22","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["A Unified Dialogue Management Strategy for Multi-intent Dialogue Conversations in Multiple Languages"],"prefix":"10.1145","volume":"20","author":[{"given":"Tulika","family":"Saha","sequence":"first","affiliation":[{"name":"Indian Institute of Technology Patna, Bihta, Bihar, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dhawal","family":"Gupta","sequence":"additional","affiliation":[{"name":"Indian Institute of Technology Patna, Bihta, Bihar, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sriparna","family":"Saha","sequence":"additional","affiliation":[{"name":"Indian Institute of Technology Patna, Bihta, Bihar, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pushpak","family":"Bhattacharyya","sequence":"additional","affiliation":[{"name":"Indian Institute of Technology Patna, Bihta, Bihar, India"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2021,9,20]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/N18-2112"},{"key":"e_1_2_1_2_1","unstructured":"Qian Chen Zhu Zhuo and Wen Wang. 2019. BERT for joint intent classification and slot filling. Retrieved from https:\/\/arXiv:1902.10909.  Qian Chen Zhu Zhuo and Wen Wang. 2019. BERT for joint intent classification and slot filling. Retrieved from https:\/\/arXiv:1902.10909."},{"key":"e_1_2_1_3_1","volume-title":"Dialogues with Social Robots","author":"Cuay\u00e1huitl Heriberto","unstructured":"Heriberto Cuay\u00e1huitl . 2017. SimpleDS: A simple deep reinforcement learning dialogue system . In Dialogues with Social Robots . Springer , Berlin , 109\u2013118. Heriberto Cuay\u00e1huitl. 2017. SimpleDS: A simple deep reinforcement learning dialogue system. In Dialogues with Social Robots. 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Young , and David Van Dyke . 2017 . A network-based end-to-end trainable task-oriented dialogue system . In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics (EACL\u201917) . 438\u2013449. Retrieved from https:\/\/aclanthology.info\/papers\/E17-1042\/e17-1042. Lina Maria Rojas-Barahona, Milica Gasic, Nikola Mrksic, Pei-Hao Su, Stefan Ultes, Tsung-Hsien Wen, Steve J. Young, and David Van Dyke. 2017. A network-based end-to-end trainable task-oriented dialogue system. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics (EACL\u201917). 438\u2013449. Retrieved from https:\/\/aclanthology.info\/papers\/E17-1042\/e17-1042."},{"key":"e_1_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-04182-3_32"},{"key":"e_1_2_1_15_1","unstructured":"Tulika Saha Dhawal Gupta Sriparna Saha and Pushpak Bhattacharyya. 2020. A hierarchical approach for efficient multi-intent dialogue policy learning. Multimedia Tools Appl. (2020) 1\u201326.  Tulika Saha Dhawal Gupta Sriparna Saha and Pushpak Bhattacharyya. 2020. A hierarchical approach for efficient multi-intent dialogue policy learning. Multimedia Tools Appl. (2020) 1\u201326."},{"key":"e_1_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2020.113650"},{"key":"e_1_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0235367"},{"key":"e_1_2_1_18_1","volume-title":"Proceedings of the 4th International Conference on Learning Representations (ICLR\u201916)","author":"Schaul Tom","year":"2016","unstructured":"Tom Schaul , John Quan , Ioannis Antonoglou , and David Silver . 2016 . Prioritized experience replay . In Proceedings of the 4th International Conference on Learning Representations (ICLR\u201916) . Retrieved from http:\/\/arxiv.org\/abs\/1511.05952. Tom Schaul, John Quan, Ioannis Antonoglou, and David Silver. 2016. Prioritized experience replay. In Proceedings of the 4th International Conference on Learning Representations (ICLR\u201916). Retrieved from http:\/\/arxiv.org\/abs\/1511.05952."},{"key":"e_1_2_1_19_1","first-page":"17","volume-title":"Proceedings of the 18th Annual SIGdial Meeting on Discourse and Dialogue, Kristiina Jokinen, Manfred Stede, David DeVault, and Annie Louis (Eds.). Association for Computational Linguistics, 147\u2013157","author":"Su Pei-Hao","year":"1865","unstructured":"Pei-Hao Su , Pawel Budzianowski , Stefan Ultes , Milica Gasic , and Steve J. Young . 2017. Sample-efficient actor-critic reinforcement learning with supervised data for dialogue management . In Proceedings of the 18th Annual SIGdial Meeting on Discourse and Dialogue, Kristiina Jokinen, Manfred Stede, David DeVault, and Annie Louis (Eds.). Association for Computational Linguistics, 147\u2013157 . 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