{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T02:45:12Z","timestamp":1760237112432,"version":"build-2065373602"},"publisher-location":"Cham","reference-count":52,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783032080486"},{"type":"electronic","value":"9783032080493"}],"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\/978-3-032-08049-3_1","type":"book-chapter","created":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T08:21:19Z","timestamp":1760170879000},"page":"1-19","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Conversational Text-to-SQL: A Comprehensive Survey of\u00a0Paradigms, Challenges, and\u00a0Future Directions"],"prefix":"10.1007","author":[{"given":"Yufei","family":"Yang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zewu","family":"Peng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fangfang","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xu","family":"Yao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuhao","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,10,12]]},"reference":[{"issue":"1","key":"1_CR1","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1017\/S135132490000005X","volume":"1","author":"I Androutsopoulos","year":"1995","unstructured":"Androutsopoulos, I., Ritchie, G.D., Thanisch, P.: Natural language interfaces to databases-an introduction. Nat. Lang. Eng. 1(1), 29\u201381 (1995)","journal-title":"Nat. Lang. Eng."},{"key":"1_CR2","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":"1_CR3","doi-asserted-by":"crossref","unstructured":"Budzianowski, P., et al.: MultiWOZ-a large-scale multi-domain wizard-of-OZ dataset for task-oriented dialogue modelling. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pp. 5016\u20135026 (2018)","DOI":"10.18653\/v1\/D18-1547"},{"key":"1_CR4","doi-asserted-by":"crossref","unstructured":"Cai, Y., Wan, X.: IGSQL: Database schema interaction graph based neural model for context-dependent text-to-SQL generation. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 2060\u20132074 (2020)","DOI":"10.18653\/v1\/2020.emnlp-main.560"},{"key":"1_CR5","doi-asserted-by":"crossref","unstructured":"Cai, Z., et al.: STAR: SQL guided pre-training for context-dependent text-to-SQL parsing. In: Findings of the Association for Computational Linguistics: EMNLP 2022, pp. 391\u2013404 (2022)","DOI":"10.18653\/v1\/2022.findings-emnlp.89"},{"key":"1_CR6","unstructured":"Deng, M., et al.: ReFoRCE: a text-to-SQL agent with self-refinement, consensus enforcement, and column exploration. arXiv preprint arXiv:2502.00675 (2025)"},{"key":"1_CR7","unstructured":"Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171\u20134186 (2019)"},{"key":"1_CR8","doi-asserted-by":"crossref","unstructured":"Dong, M., et al.: PRACTIQ: a practical conversational text-to-SQL dataset with ambiguous and unanswerable queries. In: Proceedings of the 2025 Annual Conference of the North American Chapter of the Association for Computational Linguistics (2025)","DOI":"10.18653\/v1\/2025.naacl-long.13"},{"key":"1_CR9","doi-asserted-by":"crossref","unstructured":"Fu, Y., Ou, W., Yu, Z., Lin, Y.: MIGA: A unified multi-task generation framework for conversational text-to-SQL. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a037, pp. 13038\u201313046 (2023)","DOI":"10.1609\/aaai.v37i11.26504"},{"key":"1_CR10","unstructured":"Gur, I., et al.: DialSQL: dialogue based structured query generation. In: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers), pp. 400\u2013405 (2018)"},{"issue":"8","key":"1_CR11","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735\u20131780 (1997)","journal-title":"Neural Comput."},{"key":"1_CR12","unstructured":"Hui, B., Li, B., Li, Y., Li, W., Si, L., Cao, Z.: R$$^2$$SQL: a relation-aware and topic-aware framework for cross-domain text-to-SQL. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a035, pp. 12724\u201312732 (2021)"},{"key":"1_CR13","doi-asserted-by":"crossref","unstructured":"Jain, P., Lapata, M.: Conversational semantic parsing using dynamic context graphs. In: Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pp. 1491\u20131506 (2023)","DOI":"10.18653\/v1\/2023.emnlp-main.535"},{"key":"1_CR14","unstructured":"K2view: LLM text-to-SQL solutions: top challenges and tips. K2view Blog (2024)"},{"key":"1_CR15","unstructured":"Kamath, A., Das, R.: A survey on semantic parsing. arXiv preprint arXiv:1812.00978 (2018)"},{"key":"1_CR16","doi-asserted-by":"crossref","unstructured":"Katsogiannis-Meimarakis, G., Koutrika, G.: A survey on deep learning approaches for text-to-SQL. VLDB J. 32(4), 905\u2013936 (2023)","DOI":"10.1007\/s00778-022-00776-8"},{"issue":"4","key":"1_CR17","doi-asserted-by":"publisher","first-page":"377","DOI":"10.1016\/j.websem.2010.06.001","volume":"8","author":"E Kaufmann","year":"2010","unstructured":"Kaufmann, E., Bernstein, A.: Evaluating the usability of natural language query languages and interfaces to semantic web knowledge bases. J. Web Semant. 8(4), 377\u2013393 (2010)","journal-title":"J. Web Semant."},{"key":"1_CR18","first-page":"9459","volume":"33","author":"Retrieval-augmented generation for knowledge-intensive NLP tasks","year":"2020","unstructured":"Retrieval-augmented generation for knowledge-intensive NLP tasks: Lewis, p., p\u00e9rez, e., piktus, a., petroni, f., karpukhin, v., goyal, n., k\u00fcttler, h., ott, m., chen, w.t., conneau, a., et al. Adv. Neural. Inf. Process. Syst. 33, 9459\u20139474 (2020)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"1_CR19","doi-asserted-by":"crossref","unstructured":"Li, F., Jagadish, H.: NaLIR: an interactive natural language interface for querying relational databases. In: Proceedings of the 2014 ACM SIGMOD International Conference on Management of Data, pp. 709\u2013720 (2014)","DOI":"10.1145\/2588555.2594519"},{"key":"1_CR20","doi-asserted-by":"crossref","unstructured":"Li, J., et al.: BIRD: A big bench for large-scale database grounded text-to-SQL evaluation. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a038, pp. 19045\u201319053 (2024)","DOI":"10.1609\/aaai.v38i17.29872"},{"key":"1_CR21","doi-asserted-by":"crossref","unstructured":"Li, Y., Li, B., Li, B., Cao, Z., Li, W., Si, L.: Pay more attention to history: a context modelling strategy for conversational text-to-SQL. arXiv preprint arXiv:2112.08735 (2021)","DOI":"10.21437\/Interspeech.2022-10596"},{"key":"1_CR22","doi-asserted-by":"crossref","unstructured":"Liu, Q., Ye, Z., Yu, T., Song, L., Blunsom, P.: Augmenting multi-turn text-to-SQL datasets with self-play. In: Findings of the Association for Computational Linguistics: EMNLP 2022, pp. 405\u2013418 (2022)","DOI":"10.18653\/v1\/2022.findings-emnlp.411"},{"key":"1_CR23","doi-asserted-by":"crossref","unstructured":"Liu, X., et al.: A survey of text-to-SQL in the era of LLMS: Where are we, and where are we going? IEEE Trans. Knowl. Data Eng. 37, 5735\u20135754 (2025)","DOI":"10.1109\/TKDE.2025.3592032"},{"key":"1_CR24","first-page":"27730","volume":"35","author":"L Ouyang","year":"2022","unstructured":"Ouyang, L., et al.: Training language models to follow instructions with human feedback. Adv. Neural. Inf. Process. Syst. 35, 27730\u201327744 (2022)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"1_CR25","unstructured":"Parthasarathi, S.H.K., Zeng, L., Hakkani-T\u00fcr, D.: Conversational text-to-SQL: an odyssey into state-of-the-art and challenges ahead. arXiv preprint arXiv:2302.11054 (2023)"},{"key":"1_CR26","unstructured":"Pourreza, M., Rafiei, D.: DIN-SQL: decomposed in-context learning of text-to-SQL with self-correction. In: arXiv preprint arXiv:2304.11015 (2023)"},{"key":"1_CR27","doi-asserted-by":"crossref","unstructured":"Qi, J., et al.: RASAT: integrating relational structures into pretrained Seq2Seq model for text-to-SQL. In: Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pp. 2151\u20132163 (2022)","DOI":"10.18653\/v1\/2022.emnlp-main.211"},{"key":"1_CR28","unstructured":"Qin, B., et\u00a0al.: A survey on text-to-SQL parsing: concepts, methods, and future directions. arXiv preprint arXiv:2208.13629 (2022)"},{"issue":"1","key":"1_CR29","first-page":"5485","volume":"21","author":"C Raffel","year":"2020","unstructured":"Raffel, C., et al.: Exploring the limits of transfer learning with a unified text-to-text transformer. J. Mach. Learn. Res. 21(1), 5485\u20135551 (2020)","journal-title":"J. Mach. Learn. Res."},{"key":"1_CR30","unstructured":"Rysun: No more SQL barriers: Transforming data queries through natural language and AI. Rysun Xchange (2025)"},{"key":"1_CR31","doi-asserted-by":"crossref","unstructured":"Sennrich, R., Haddow, B., Birch, A.: Neural machine translation of rare words with subword units. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 1715\u20131725 (2016)","DOI":"10.18653\/v1\/P16-1162"},{"key":"1_CR32","doi-asserted-by":"crossref","unstructured":"Shi, L., Tang, Z., Zhang, N., Zhang, X., Yang, Z.: A survey on employing large language models for text-to-SQL tasks. ACM Comput. Surv. 58, 1\u201337 (2024)","DOI":"10.1145\/3737873"},{"key":"1_CR33","doi-asserted-by":"crossref","unstructured":"Suhr, A., Iyer, S., Artzi, Y.: Learning to map context-dependent sentences to executable formal queries. In: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), pp. 839\u2013850 (2018)","DOI":"10.18653\/v1\/N18-1203"},{"key":"1_CR34","unstructured":"Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. In: Advances in Neural Information Processing Systems, vol. 27 (2014)"},{"key":"1_CR35","doi-asserted-by":"crossref","unstructured":"Wang, B., Shin, R., Liu, X., Polozov, O., Richardson, M.: RAT-SQL: relation-aware schema encoding and linking for text-to-SQL parsers. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 7567\u20137578 (2020)","DOI":"10.18653\/v1\/2020.acl-main.677"},{"key":"1_CR36","unstructured":"Wang, B., et al.: MAC-SQL: a multi-agent collaborative framework for text-to-SQL. In: Proceedings of the 2025 International Conference on Computational Linguistics (2025)"},{"key":"1_CR37","unstructured":"Xu, X., Liu, C., Song, D.: SQLNet: generating structured queries from natural language without reinforcement learning. arXiv preprint arXiv:1711.04436 (2017)"},{"key":"1_CR38","unstructured":"Yao, Z., Zhang, Y., Zhang, R., Radev, D.: STEPS: a human-in-the-loop framework for guiding and refining text-to-SQL generation. In: Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pp. 1016\u20131032 (2024)"},{"key":"1_CR39","doi-asserted-by":"crossref","unstructured":"Yu, T., et al.: SyntaxSQLNet: syntax tree networks for complex and cross-domain text-to-SQL task. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pp. 3403\u20133413 (2018)","DOI":"10.18653\/v1\/D18-1193"},{"key":"1_CR40","doi-asserted-by":"crossref","unstructured":"Yu, T., et al.: CoSQL: a conversational text-to-SQL challenge towards cross-domain natural language interfaces to databases. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 1962\u20131979 (2019)","DOI":"10.18653\/v1\/D19-1204"},{"key":"1_CR41","unstructured":"Yu, T., Zhang, R., Polozov, A., Meek, C., Awadallah, A.: SCoRe: pre-training for context representation in conversational semantic parsing. In: International Conference on Learning Representations (2021)"},{"key":"1_CR42","doi-asserted-by":"crossref","unstructured":"Yu, T., et\u00a0al.: Spider: a large-scale human-labeled dataset for complex and cross-domain semantic parsing and text-to-SQL task. arXiv preprint arXiv:1809.08887 (2018)","DOI":"10.18653\/v1\/D18-1425"},{"key":"1_CR43","doi-asserted-by":"crossref","unstructured":"Yu, T., et al.: SParC: cross-domain semantic parsing in context. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 4526\u20134535 (2019)","DOI":"10.18653\/v1\/P19-1443"},{"key":"1_CR44","unstructured":"Zelle, J.M., Mooney, R.J.: Learning to parse database queries using inductive logic programming. In: Proceedings of the National Conference on Artificial Intelligence, pp. 1050\u20131055 (1996)"},{"key":"1_CR45","unstructured":"Zettlemoyer, L., Collins, M.: Learning to map sentences to logical form: structured classification with probabilistic categorial grammars. In: Proceedings of the Uncertainty in Artificial Intelligence (2012)"},{"key":"1_CR46","doi-asserted-by":"crossref","unstructured":"Zhang, H., Cao, R., Xu, H., Chen, L., Yu, K.: CoE-SQL: In-context learning for multi-turn text-to-SQL with chain-of-editions. In: Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 6487\u20136508 (2024)","DOI":"10.18653\/v1\/2024.naacl-long.361"},{"key":"1_CR47","doi-asserted-by":"crossref","unstructured":"Zhang, J., et al.: Next-generation database interfaces: a survey of LLM-based text-to-SQL. arXiv preprint arXiv:2406.08426 (2024)","DOI":"10.1109\/TKDE.2025.3609486"},{"key":"1_CR48","doi-asserted-by":"crossref","unstructured":"Zhang, R., et al.: Editing-based SQL query generation for cross-domain context-dependent questions. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4309\u20134319 (2019)","DOI":"10.18653\/v1\/D19-1537"},{"key":"1_CR49","doi-asserted-by":"crossref","unstructured":"Zheng, Y., Wang, H., Dong, B., Wang, X., Li, C.: HIE-SQL: history information enhanced network for context-dependent text-to-SQL semantic parsing. In: Findings of the Association for Computational Linguistics: ACL 2022, pp. 3546\u20133556 (2022)","DOI":"10.18653\/v1\/2022.findings-acl.236"},{"key":"1_CR50","doi-asserted-by":"crossref","unstructured":"Zheng, Z.: Question answering using web news as knowledge base. In: Proceedings of the tenth conference on European chapter of the Association for Computational Linguistics, vol.\u00a02, pp. 243\u2013246 (2003)","DOI":"10.3115\/1067737.1067797"},{"key":"1_CR51","unstructured":"Zhong, V., Xiong, C., Socher, R.: Seq2SQL: generating structured queries from natural language using reinforcement learning. arXiv preprint arXiv:1709.00103 (2017)"},{"key":"1_CR52","unstructured":"Zhu, X., Li, Q., Cui, L., Liu, Y.: Large language model enhanced text-to-SQL generation: a survey. arXiv preprint arXiv:2401.06011 (2024)"}],"container-title":["Communications in Computer and Information Science","Agents and Robots for reliable Engineered Autonomy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-08049-3_1","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:03:14Z","timestamp":1760173394000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-08049-3_1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9783032080486","9783032080493"],"references-count":52,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-08049-3_1","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"12 October 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"AREA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Workshop on Agents and Robots for reliable Engineered Autonomy","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Bologna","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":"25 October 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25 October 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"area2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/areaworkshop.github.io\/AREA2025\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}