{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T11:03:33Z","timestamp":1772363013807,"version":"3.50.1"},"publisher-location":"Cham","reference-count":45,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031887192","type":"print"},{"value":"9783031887208","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025]]},"DOI":"10.1007\/978-3-031-88720-8_37","type":"book-chapter","created":{"date-parts":[[2025,4,4]],"date-time":"2025-04-04T12:05:29Z","timestamp":1743768329000},"page":"233-239","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Towards Intent-Driven Transparency in\u00a0Conversational Search Systems"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2105-8477","authenticated-orcid":false,"given":"Yumeng","family":"Wang","sequence":"first","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,4,3]]},"reference":[{"key":"37_CR1","doi-asserted-by":"crossref","first-page":"468","DOI":"10.1162\/tacl_a_00471","volume":"10","author":"V Adlakha","year":"2022","unstructured":"Adlakha, V., Dhuliawala, S., Suleman, K., de Vries, H., Reddy, S.: Topiocqa: open-domain conversational question answering with topic switching. Trans. Assoc. Comput. Linguist. 10, 468\u2013483 (2022)","journal-title":"Trans. Assoc. Comput. Linguist."},{"key":"37_CR2","unstructured":"Anand, A., Anand, A., Setty, V., et\u00a0al.: Query understanding in the age of large language models. arXiv preprint arXiv:2306.16004 (2023)"},{"key":"37_CR3","doi-asserted-by":"crossref","unstructured":"Anantha, R., Vakulenko, S., Tu, Z., Longpre, S., Pulman, S., Chappidi, S.: Open-domain question answering goes conversational via question rewriting. arXiv preprint arXiv:2010.04898 (2020)","DOI":"10.18653\/v1\/2021.naacl-main.44"},{"key":"37_CR4","unstructured":"Beitzel, S.M., Jensen, E.C., Frieder, O., Lewis, D.D., Chowdhury, A., Kolcz, A.: Improving automatic query classification via semi-supervised learning. In: Fifth IEEE International Conference on Data Mining (ICDM\u201905), p. 8. IEEE (2005)"},{"key":"37_CR5","doi-asserted-by":"crossref","unstructured":"Broder, A.: A taxonomy of web search. In: ACM SIGIR Forum, vol.\u00a036, pp. 3\u201310. ACM, New York (2002)","DOI":"10.1145\/792550.792552"},{"key":"37_CR6","first-page":"1877","volume":"33","author":"T Brown","year":"2020","unstructured":"Brown, T., et al.: Language models are few-shot learners. Adv. Neural. Inf. Process. Syst. 33, 1877\u20131901 (2020)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"37_CR7","doi-asserted-by":"crossref","unstructured":"Chen, H., Dou, Z., Mao, K., Liu, J., Zhao, Z.: Generalizing conversational dense retrieval via llm-cognition data augmentation. arXiv preprint arXiv:2402.07092 (2024)","DOI":"10.18653\/v1\/2024.acl-long.149"},{"key":"37_CR8","doi-asserted-by":"crossref","unstructured":"Chen, H., Dou, Z., Zhu, Y., Cao, Z., Cheng, X., Wen, J.R.: Enhancing user behavior sequence modeling by generative tasks for session search. In: Proceedings of the 31st ACM International Conference on Information & Knowledge Management, pp. 180\u2013190 (2022)","DOI":"10.1145\/3511808.3557310"},{"key":"37_CR9","doi-asserted-by":"publisher","unstructured":"Cheng, Y., Mao, K., Dou, Z.: Interpreting conversational dense retrieval by rewriting-enhanced inversion of session embedding. In: Ku, L.W., Martins, A., Srikumar, V. (eds.) Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics, vol. 1: Long Papers, pp. 2879\u20132893. Association for Computational Linguistics, Bangkok (2024). https:\/\/doi.org\/10.18653\/v1\/2024.acl-long.159. https:\/\/aclanthology.org\/2024.acl-long.159","DOI":"10.18653\/v1\/2024.acl-long.159"},{"key":"37_CR10","doi-asserted-by":"crossref","unstructured":"Formal, T., Piwowarski, B., Clinchant, S.: Splade: sparse lexical and expansion model for first stage ranking. In: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 2288\u20132292 (2021)","DOI":"10.1145\/3404835.3463098"},{"key":"37_CR11","doi-asserted-by":"crossref","unstructured":"Gao, J., Xiong, C., Bennett, P., Craswell, N.: Neural Approaches to Conversational Information Retrieval. Springer, Heidelberg (2023)","DOI":"10.1007\/978-3-031-23080-6"},{"key":"37_CR12","doi-asserted-by":"crossref","unstructured":"Guo, J., Lan, Y.: Query classification. In: Query Understanding for Search Engines, pp. 15\u201341 (2020)","DOI":"10.1007\/978-3-030-58334-7_2"},{"key":"37_CR13","doi-asserted-by":"crossref","unstructured":"Hong, Y., Vaidya, J., Lu, H., Liu, W.M.: Accurate and efficient query clustering via top ranked search results. In: Web Intelligence, vol.\u00a014, pp. 119\u2013138. IOS Press (2016)","DOI":"10.3233\/WEB-160335"},{"key":"37_CR14","doi-asserted-by":"crossref","unstructured":"Izacard, G., Grave, E.: Leveraging passage retrieval with generative models for open domain question answering. arXiv preprint arXiv:2007.01282 (2020)","DOI":"10.18653\/v1\/2021.eacl-main.74"},{"key":"37_CR15","doi-asserted-by":"crossref","unstructured":"Jeong, S., Baek, J., Hwang, S.J., Park, J.C.: Phrase retrieval for open-domain conversational question answering with conversational dependency modeling via contrastive learning. arXiv preprint arXiv:2306.04293 (2023)","DOI":"10.18653\/v1\/2023.findings-acl.374"},{"key":"37_CR16","doi-asserted-by":"crossref","unstructured":"Lewis, M.: Bart: denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension. arXiv preprint arXiv:1910.13461 (2019)","DOI":"10.18653\/v1\/2020.acl-main.703"},{"key":"37_CR17","doi-asserted-by":"crossref","unstructured":"Li, J., et al.: Graph enhanced bert for query understanding. In: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 3315\u20133319 (2023)","DOI":"10.1145\/3539618.3591845"},{"issue":"2","key":"37_CR18","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1145\/1117454.1117466","volume":"7","author":"Y Li","year":"2005","unstructured":"Li, Y., Zheng, Z., Dai, H.: Kdd cup-2005 report: facing a great challenge. ACM SIGKDD Explorat. Newsl 7(2), 91\u201399 (2005)","journal-title":"ACM SIGKDD Explorat. Newsl"},{"key":"37_CR19","unstructured":"Liu, Q., Wang, B., Wang, N., Mao, J.: Leveraging passage embeddings for efficient listwise reranking with large language models. arXiv preprint arXiv:2406.14848 (2024)"},{"key":"37_CR20","doi-asserted-by":"crossref","unstructured":"Lu, C., Li, L., Kim, D., Wang, X., Shen, R.: An effective, efficient, and stable framework for query clustering. In: 2024 IEEE 40th International Conference on Data Engineering (ICDE), pp. 5334\u20135340. IEEE (2024)","DOI":"10.1109\/ICDE60146.2024.00402"},{"key":"37_CR21","doi-asserted-by":"crossref","unstructured":"Ma, X., Gong, Y., He, P., Zhao, H., Duan, N.: Query rewriting for retrieval-augmented large language models. arXiv preprint arXiv:2305.14283 (2023)","DOI":"10.18653\/v1\/2023.emnlp-main.322"},{"key":"37_CR22","doi-asserted-by":"publisher","unstructured":"Mao, K., et al.: Search-oriented conversational query editing. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Findings of the Association for Computational Linguistics: ACL 2023, pp. 4160\u20134172. Association for Computational Linguistics, Toronto (2023). https:\/\/doi.org\/10.18653\/v1\/2023.findings-acl.256. https:\/\/aclanthology.org\/2023.findings-acl.256","DOI":"10.18653\/v1\/2023.findings-acl.256"},{"key":"37_CR23","doi-asserted-by":"crossref","unstructured":"Mao, K., Dou, Z., Mo, F., Hou, J., Chen, H., Qian, H.: Large language models know your contextual search intent: a prompting framework for conversational search. arXiv preprint arXiv:2303.06573 (2023)","DOI":"10.18653\/v1\/2023.findings-emnlp.86"},{"key":"37_CR24","doi-asserted-by":"crossref","unstructured":"Mao, K., Dou, Z., Qian, H.: Curriculum contrastive context denoising for few-shot conversational dense retrieval. In: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 176\u2013186 (2022)","DOI":"10.1145\/3477495.3531961"},{"key":"37_CR25","doi-asserted-by":"crossref","unstructured":"Mao, K., et al.: Learning denoised and interpretable session representation for conversational search. In: Proceedings of the ACM Web Conference 2023, pp. 3193\u20133202 (2023)","DOI":"10.1145\/3543507.3583265"},{"key":"37_CR26","doi-asserted-by":"publisher","unstructured":"Mao, Y., et al.: Generation-augmented retrieval for open-domain question answering. In: Zong, C., Xia, F., Li, W., Navigli, R. (eds.) Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, vol. 1: Long Papers, pp. 4089\u20134100. Association for Computational Linguistics, Online (2021). https:\/\/doi.org\/10.18653\/v1\/2021.acl-long.316. https:\/\/aclanthology.org\/2021.acl-long.316","DOI":"10.18653\/v1\/2021.acl-long.316"},{"key":"37_CR27","unstructured":"Mo, F., et al.: A survey of conversational search. arXiv preprint arXiv:2410.15576 (2024)"},{"key":"37_CR28","doi-asserted-by":"crossref","unstructured":"Mo, F., Mao, K., Zhu, Y., Wu, Y., Huang, K., Nie, J.Y.: Convgqr: generative query reformulation for conversational search. arXiv preprint arXiv:2305.15645 (2023)","DOI":"10.18653\/v1\/2023.acl-long.274"},{"key":"37_CR29","doi-asserted-by":"crossref","unstructured":"Mo, F., et al.: Learning to relate to previous turns in conversational search. In: Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 1722\u20131732 (2023)","DOI":"10.1145\/3580305.3599411"},{"key":"37_CR30","doi-asserted-by":"crossref","unstructured":"Mo, F., et al.: History-aware conversational dense retrieval. arXiv preprint arXiv:2401.16659 (2024)","DOI":"10.18653\/v1\/2024.findings-acl.792"},{"key":"37_CR31","doi-asserted-by":"crossref","unstructured":"Morris, J.X., Kuleshov, V., Shmatikov, V., Rush, A.M.: Text embeddings reveal (almost) as much as text. arXiv preprint arXiv:2310.06816 (2023)","DOI":"10.18653\/v1\/2023.emnlp-main.765"},{"key":"37_CR32","unstructured":"Rafailov, R., Sharma, A., Mitchell, E., Manning, C.D., Ermon, S., Finn, C.: Direct preference optimization: your language model is secretly a reward model. Adv. Neural Inf. Process. Syst. 36 (2024)"},{"key":"37_CR33","series-title":"Advances in Intelligent Systems and Computing","doi-asserted-by":"publisher","first-page":"261","DOI":"10.1007\/978-981-13-3600-3_25","volume-title":"Soft Computing and Signal Processing","author":"MS Rani","year":"2019","unstructured":"Rani, M.S., Babu, G.C.: Efficient query clustering technique and context well-informed document clustering. In: Wang, J., Reddy, G., Prasad, V.K., Reddy, V.S. (eds.) Soft Computing and Signal Processing. AISC, vol. 900, pp. 261\u2013271. Springer, Singapore (2019). https:\/\/doi.org\/10.1007\/978-981-13-3600-3_25"},{"key":"37_CR34","doi-asserted-by":"publisher","unstructured":"Roberts, A., Raffel, C., Shazeer, N.: How much knowledge can you pack into the parameters of a language model? In: Webber, B., Cohn, T., He, Y., Liu, Y. (eds.) Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 5418\u20135426. Association for Computational Linguistics, Online (2020). https:\/\/doi.org\/10.18653\/v1\/2020.emnlp-main.437. https:\/\/aclanthology.org\/2020.emnlp-main.437","DOI":"10.18653\/v1\/2020.emnlp-main.437"},{"key":"37_CR35","doi-asserted-by":"crossref","unstructured":"Rose, D.E., Levinson, D.: Understanding user goals in web search. In: Proceedings of the 13th International Conference on World Wide Web, pp. 13\u201319 (2004)","DOI":"10.1145\/988672.988675"},{"key":"37_CR36","doi-asserted-by":"crossref","unstructured":"Vakulenko, S., Longpre, S., Tu, Z., Anantha, R.: Question rewriting for conversational question answering. In: Proceedings of the 14th ACM International Conference on Web Search and Data Mining, pp. 355\u2013363 (2021)","DOI":"10.1145\/3437963.3441748"},{"key":"37_CR37","doi-asserted-by":"crossref","unstructured":"Voskarides, N., Li, D., Ren, P., Kanoulas, E., de\u00a0Rijke, M.: Query resolution for conversational search with limited supervision. In: Proceedings of the 43rd International ACM SIGIR conference on research and development in Information Retrieval, pp. 921\u2013930 (2020)","DOI":"10.1145\/3397271.3401130"},{"key":"37_CR38","doi-asserted-by":"publisher","unstructured":"Wang, L., Yang, N., Wei, F.: Query2doc: query expansion with large language models. In: Bouamor, H., Pino, J., Bali, K. (eds.) Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pp. 9414\u20139423. Association for Computational Linguistics, Singapore (2023). https:\/\/doi.org\/10.18653\/v1\/2023.emnlp-main.585. https:\/\/aclanthology.org\/2023.emnlp-main.585","DOI":"10.18653\/v1\/2023.emnlp-main.585"},{"key":"37_CR39","unstructured":"Wang, Y., Chen, X., Verberne, S.: QUIDS: Query intent generation via dual space modeling. arXiv preprint arXiv:2410.12400 (2024)"},{"issue":"1","key":"37_CR40","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1145\/503104.503108","volume":"20","author":"JR Wen","year":"2002","unstructured":"Wen, J.R., Nie, J.Y., Zhang, H.J.: Query clustering using user logs. ACM Trans. Inf. Syst. 20(1), 59\u201381 (2002)","journal-title":"ACM Trans. Inf. Syst."},{"key":"37_CR41","doi-asserted-by":"crossref","unstructured":"Yu, S., Liu, J., Yang, J., Xiong, C., Bennett, P., Gao, J., Liu, Z.: Few-shot generative conversational query rewriting. In: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 1933\u20131936 (2020)","DOI":"10.1145\/3397271.3401323"},{"key":"37_CR42","doi-asserted-by":"crossref","unstructured":"Zamani, H., Trippas, J.R., Dalton, J., Radlinski, F., et\u00a0al.: Conversational information seeking. Found. Trends\u00ae Inf. Retr. 17(3-4), 244\u2013456 (2023)","DOI":"10.1561\/1500000081"},{"key":"37_CR43","doi-asserted-by":"crossref","unstructured":"Zhang, H., Xu, H., Lin, T.E., Lyu, R.: Discovering new intents with deep aligned clustering. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a035, pp. 14365\u201314373 (2021)","DOI":"10.1609\/aaai.v35i16.17689"},{"key":"37_CR44","doi-asserted-by":"crossref","unstructured":"Zhang, H., Xu, H., Wang, X., Long, F., Gao, K.: A clustering framework for unsupervised and semi-supervised new intent discovery. IEEE Trans. Knowl. Data Eng. (2023)","DOI":"10.1109\/TKDE.2023.3340732"},{"key":"37_CR45","doi-asserted-by":"crossref","unstructured":"Zhang, R., Guo, J., Fan, Y., Lan, Y., Cheng, X.: Query understanding via intent description generation. In: Proceedings of the 29th ACM International Conference on Information & Knowledge Management, pp. 1823\u20131832 (2020)","DOI":"10.1145\/3340531.3411999"}],"container-title":["Lecture Notes in Computer Science","Advances in Information Retrieval"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-88720-8_37","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,4]],"date-time":"2025-04-04T12:05:47Z","timestamp":1743768347000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-88720-8_37"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9783031887192","9783031887208"],"references-count":45,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-88720-8_37","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"3 April 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECIR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Conference on Information Retrieval","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Lucca","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7 April 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11 April 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"47","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ecir2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ecir2025.eu\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}