{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,9]],"date-time":"2026-06-09T04:01:14Z","timestamp":1780977674037,"version":"3.54.1"},"reference-count":25,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"National Key Research and Development Program of China","award":["2022YFF0503900"],"award-info":[{"award-number":["2022YFF0503900"]}]},{"DOI":"10.13039\/100014103","name":"Key Research and Development Program of Shandong Province","doi-asserted-by":"crossref","award":["2021CXGC010104"],"award-info":[{"award-number":["2021CXGC010104"]}],"id":[{"id":"10.13039\/100014103","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Fuzzy Optim Decis Making"],"published-print":{"date-parts":[[2026,6]]},"DOI":"10.1007\/s10700-026-09478-0","type":"journal-article","created":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T06:35:50Z","timestamp":1780295750000},"page":"315-331","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Structure-aware neural generation of text from SQL queries"],"prefix":"10.1007","volume":"25","author":[{"given":"Jianwen","family":"Yang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dawei","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiali","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jian","family":"Miao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuo","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinguo","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xueyu","family":"Gao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinjie","family":"Lv","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhaoxin","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rongxue","family":"Kang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,6,1]]},"reference":[{"key":"9478_CR1","unstructured":"Al-Lawati, A., Lucas, J., & Mitra, P. (2025). Semantic captioning: benchmark dataset and graph-aware few-shot in-context learning for SQL2Text. In Proceedings of the 31st International Conference on Computational Linguistics (pp. 8026-8042). Abu Dhabi, UAE."},{"key":"9478_CR2","unstructured":"Bahdanau, D., Cho, K., & Bengio, Y. (2014). Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473."},{"key":"9478_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2025.128293","volume":"289","author":"M Bhardwaj","year":"2025","unstructured":"Bhardwaj, M., Ethari, H., & Moirangthem, D. S. (2025). EzSQL: An SQL intermediate representation for improving SQL-to-text generation. Expert Systems with Applications, 289, Article 128293.","journal-title":"Expert Systems with Applications"},{"key":"9478_CR4","doi-asserted-by":"crossref","unstructured":"Devlin, J., Chang, M.W., Lee, K., & Toutanova, K. (2019). 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. 4171-4186). Minneapolis, Minnesota.","DOI":"10.18653\/v1\/N19-1423"},{"key":"9478_CR5","doi-asserted-by":"crossref","unstructured":"Dong, L., & Lapata, M. (2016). Language to logical form with neural attention. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (pp. 33-43). Berlin, Germany.","DOI":"10.18653\/v1\/P16-1004"},{"key":"9478_CR6","doi-asserted-by":"crossref","unstructured":"Eriguchi, A., Hashimoto, K., & Tsuruoka, Y. (2016). Tree-to-sequence attentional neural machine translation. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (pp. 823-833). Berlin, Germany.","DOI":"10.18653\/v1\/P16-1078"},{"key":"9478_CR7","doi-asserted-by":"crossref","unstructured":"Finegan-Dollak, C., Kummerfeld, J.K., Zhang, L., Ramanathan, K., Sadasivam, S., Zhang, R., & Radev, D. (2018). Improving text-to-SQL evaluation methodology. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (pp. 351-360). Melbourne, Australia.","DOI":"10.18653\/v1\/P18-1033"},{"key":"9478_CR9","doi-asserted-by":"crossref","unstructured":"Iyer, S., Konstas, I., Cheung, A., Krishnamurthy, J., & Zettlemoyer, L. (2017). Learning a neural semantic parser from user feedback. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (pp. 963-973). Vancouver, Canada.","DOI":"10.18653\/v1\/P17-1089"},{"key":"9478_CR8","doi-asserted-by":"crossref","unstructured":"Iyer, S., Konstas, I., Cheung, A., & Zettlemoyer, L. (2016). Summarizing source code using a neural attention model. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (pp. 2073-2083). Berlin, Germany.","DOI":"10.18653\/v1\/P16-1195"},{"key":"9478_CR26","doi-asserted-by":"crossref","unstructured":"Koutrika, G., Simitsis, A., & Ioannidis, Y. E. (2010). Explaining structured queries in natural language. In 2010 IEEE 26th International Conference on Data Engineering (ICDE 2010) (pp. 333-344). IEEE. Long Beach, CA, USA.","DOI":"10.1109\/ICDE.2010.5447824"},{"key":"9478_CR10","doi-asserted-by":"crossref","unstructured":"Lei, T., Barzilay, R., & Jaakkola, T. (2016). Rationalizing neural predictions. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing (pp. 107-117). Austin, Texas.","DOI":"10.18653\/v1\/D16-1011"},{"key":"9478_CR11","doi-asserted-by":"crossref","unstructured":"Lewis, M., Liu, Y., Goyal, N., Ghazvininejad, M., Mohamed, A., Levy, O., Stoyanov, V., & Zettlemoyer, L. (2020). BART: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics  (pp. 7871-7880). Online.","DOI":"10.18653\/v1\/2020.acl-main.703"},{"key":"9478_CR12","volume-title":"Uncertainty Theory","author":"B Liu","year":"2007","unstructured":"Liu, B. (2007). Uncertainty Theory (2nd ed). Springer.","edition":"2nd ed"},{"issue":"1","key":"9478_CR13","first-page":"3","volume":"3","author":"B Liu","year":"2009","unstructured":"Liu, B. (2009). Some research problems in uncertainty theory. Journal of Uncertain Systems, 3(1), 3\u201310.","journal-title":"Journal of Uncertain Systems"},{"issue":"2","key":"9478_CR14","first-page":"83","volume":"4","author":"B Liu","year":"2010","unstructured":"Liu, B. (2010). Uncertain set theory and uncertain inference rule with application to uncertain control. Journal of Uncertain Systems, 4(2), 83\u201398.","journal-title":"Journal of Uncertain Systems"},{"key":"9478_CR27","doi-asserted-by":"crossref","unstructured":"Ngonga Ngomo, A. C., B\u00fchmann, L., Unger, C., Lehmann, J., & Gerber, D. (2013). SPARQL2NL: Verbalizing SPARQL queries. In Proceedings of the 22nd International World Wide Web Conference Companion (WWW 13 Companion) (pp. 329-332). ACM. Rio de Janeiro, Brazil.","DOI":"10.1145\/2487788.2487936"},{"key":"9478_CR15","doi-asserted-by":"crossref","unstructured":"Papineni, K., Roukos, S., Ward, T., & Zhu W.J. (2002). BLEU: a method for automatic evaluation of machine translation. In Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics (pp. 311-318). Philadelphia.","DOI":"10.3115\/1073083.1073135"},{"key":"9478_CR16","doi-asserted-by":"crossref","unstructured":"Rubin, O., & Berant, J. (2021). SmBoP: Semi-autoregressive bottom-up semantic parsing. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (pp. 311-324). Online.","DOI":"10.18653\/v1\/2021.naacl-main.29"},{"key":"9478_CR17","doi-asserted-by":"crossref","unstructured":"See, A., Liu, P.J., & Manning, C.D. (2017). Get to the point: Summarization with pointer-generator networks. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (pp. 1073-1083). Vancouver, Canada.","DOI":"10.18653\/v1\/P17-1099"},{"key":"9478_CR20","unstructured":"Wang, C., Tatwawadi, K., Brockschmidt, M., Huang, P.S., Mao, Y., Polozov, O., & Singh, R. (2018). Robust text-to-SQL generation with execution-guided decoding. arXiv preprint arXiv:1807.03100."},{"key":"9478_CR21","doi-asserted-by":"crossref","unstructured":"Xu, K., Wu, L., Wang, Z., Feng, Y., & Sheinin, V. (2018). SQL-to-Text Generation with Graph-to-Sequence Model. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (pp. 931-936). Brussels, Belgium.","DOI":"10.18653\/v1\/D18-1112"},{"key":"9478_CR22","unstructured":"Xu, X., Liu, C., Song, D. (2017). SQLNet: Generating structured queries from natural language without reinforcement learning. arXiv preprint arXiv:1711.04436."},{"key":"9478_CR23","doi-asserted-by":"crossref","unstructured":"Yin, P., & Neubig, G. (2018). TRANX: A transition-based neural abstract syntax parser for semantic parsing and code generation. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: System demonstrations (pp. 7-12). Brussels, Belgium.","DOI":"10.18653\/v1\/D18-2002"},{"key":"9478_CR24","doi-asserted-by":"crossref","unstructured":"Yu, T., Zhang, R., Yang, K., Yasunaga, M., Wang, D., Li, Z., Ma, Q., Li, I., Yao, S., Roman, S., Zhang, Z., & Radev D. (2018). Spider: A large-scale human-labeled dataset for complex and cross-domain semantic parsing and text-to-SQL task. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing.","DOI":"10.18653\/v1\/D18-1425"},{"key":"9478_CR25","unstructured":"Zhong, V., Xiong, C., & Socher, R. (2017). Seq2SQL: Generating structured queries from natural language using reinforcement learning. arXiv preprint arXiv:1709.00103."}],"container-title":["Fuzzy Optimization and Decision Making"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10700-026-09478-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10700-026-09478-0","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10700-026-09478-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,9]],"date-time":"2026-06-09T03:50:51Z","timestamp":1780977051000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10700-026-09478-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6]]},"references-count":25,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2026,6]]}},"alternative-id":["9478"],"URL":"https:\/\/doi.org\/10.1007\/s10700-026-09478-0","relation":{},"ISSN":["1568-4539","1573-2908"],"issn-type":[{"value":"1568-4539","type":"print"},{"value":"1573-2908","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6]]},"assertion":[{"value":"17 March 2026","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 March 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 June 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}