{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,20]],"date-time":"2026-02-20T19:06:58Z","timestamp":1771614418390,"version":"3.50.1"},"publisher-location":"Cham","reference-count":38,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031624940","type":"print"},{"value":"9783031624957","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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":[[2024]]},"DOI":"10.1007\/978-3-031-62495-7_12","type":"book-chapter","created":{"date-parts":[[2024,6,21]],"date-time":"2024-06-21T20:19:24Z","timestamp":1719001164000},"page":"152-165","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Enhancing Natural Language Query to SQL Query Generation Through Classification-Based Table Selection"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9970-8038","authenticated-orcid":false,"given":"Ankush","family":"Chopra","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0001-9084-0884","authenticated-orcid":false,"given":"Rauful","family":"Azam","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,6,22]]},"reference":[{"key":"12_CR1","unstructured":"Abraham, A., Rahman, F., Kaur, D.: TableQuery: querying tabular data with natural language. ArXiv abs\/2202.00454 (2022)"},{"key":"12_CR2","unstructured":"Chen, W., Chang, M.W., Schlinger, E., Wang, W.Y., Cohen, W.W.: Open question answering over tables and text. ArXiv abs\/2010.10439 (2021)"},{"key":"12_CR3","doi-asserted-by":"crossref","unstructured":"Chen, W., Zha, H., Chen, Z., Xiong, W., Wang, H., Wang, W.Y.: HybridQA: a dataset of multi-hop question answering over tabular and textual data. In: FINDINGS (2020)","DOI":"10.18653\/v1\/2020.findings-emnlp.91"},{"key":"12_CR4","unstructured":"Chen, X., Lin, M., Scharli, N., Zhou, D.: Teaching large language models to self-debug. ArXiv abs\/2304.05128 (2023)"},{"key":"12_CR5","unstructured":"Cho, M., Amplayo, R.K., Hwang, S.W., Park, J.: Adversarial TableQA: attention supervision for question answering on tables. In: PMLR (2018)"},{"key":"12_CR6","unstructured":"Defog. SQLCoder (2023). https:\/\/defog.ai\/sqlcoder-demo\/"},{"key":"12_CR7","unstructured":"Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. ArXiv abs\/1810.04805 (2019)"},{"key":"12_CR8","unstructured":"He, P., Liu, X., Gao, J., Chen, W.: DeBERTa: decoding-enhanced BERT with disentangled attention. Arxiv abs\/2006.03654 (2021)"},{"key":"12_CR9","doi-asserted-by":"crossref","unstructured":"Herzig, J., Nowak, P.K., M\u00fcller, T., Piccinno, F., Eisenschlos, J.M.: TAPAS: weakly supervised table parsing via pre-training. ArXiv abs\/2004.02349 (2020)","DOI":"10.18653\/v1\/2020.acl-main.398"},{"key":"12_CR10","doi-asserted-by":"crossref","unstructured":"Katsis, Y., et al.: AIT-QA: question answering dataset over complex tables in the airline industry. ArXiv abs\/2106.12944 (2021)","DOI":"10.18653\/v1\/2022.naacl-industry.34"},{"key":"12_CR11","unstructured":"Lei, F., et al.: TableQAKit: a comprehensive and practical toolkit for table-based question answering. ArXiv abs\/2310.15075 (2023)"},{"key":"12_CR12","doi-asserted-by":"crossref","unstructured":"Levy, I., Bogin, B., Berant, J.: Diverse demonstrations improve in-context compositional generalization. ArXiv abs\/2212.06800 (2023)","DOI":"10.18653\/v1\/2023.acl-long.78"},{"key":"12_CR13","unstructured":"Liu, Q., et al.: TAPEX: table pre-training via learning a neural SQL executor. ArXiv abs\/2107.07653 (2022)"},{"key":"12_CR14","unstructured":"NSQL. Numbers Station Text to SQL model code. GitHub - NumbersStationAI\/NSQL: Numbers Station Text to SQL model code (2023)"},{"key":"12_CR15","unstructured":"OpenAI. Introducing chatgpt (2022). https:\/\/openai.com\/blog\/chatgpt"},{"key":"12_CR16","unstructured":"OpenAI. Embeddings (2022). https:\/\/platform.openai.com\/docs\/guides\/embeddings\/what-are-embeddings"},{"key":"12_CR17","unstructured":"OpenAI. GPT-4 technical report. ArXiv, abs\/2303.08774 (2023)"},{"key":"12_CR18","unstructured":"Piktus, A., et al.: Retrieval-augmented generation for knowledge-intensive NLP tasks. ArXiv abs\/2005.11401 (2021)"},{"key":"12_CR19","unstructured":"Poesia, G., Polozov, O.: Synchromesh: reliable code generation from pre-trained language models. ArXiv abs\/2201.11227 (2022)"},{"key":"12_CR20","unstructured":"Pourreza, M., Rafiei, D.: DIN-SQL: decomposed in-context learning of text-to-SQL with self-correction. ArXiv abs\/2304.11015 (2023)"},{"key":"12_CR21","doi-asserted-by":"publisher","unstructured":"Prasad, A.: Enhancement of natural language to SQL query conversion using machine learning techniques. Int. J. Adv. Comput. Sci. Appl. 11, 494\u2013503 (2020). https:\/\/doi.org\/10.14569\/IJACSA.2020.0111260","DOI":"10.14569\/IJACSA.2020.0111260"},{"key":"12_CR22","unstructured":"Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I.: Language models are unsupervised multitask learners. https:\/\/d4mucfpksywv.cloudfront.net\/better-language-models\/language-models.pdf"},{"key":"12_CR23","unstructured":"Ren, R., et al.: RocketQAv2: a joint training method for dense passage retrieval and passage re-ranking. ArXiv abs\/2110.07367 (2023)"},{"key":"12_CR24","doi-asserted-by":"crossref","unstructured":"Rubin, O., Herzig, J., Berant, J.: Learning to retrieve prompts for in-context learning. ArXiv abs\/2112.08633 (2022)","DOI":"10.18653\/v1\/2022.naacl-main.191"},{"key":"12_CR25","unstructured":"SBERT.net. Sentence transformers (2022). https:\/\/www.sbert.net\/"},{"key":"12_CR26","doi-asserted-by":"crossref","unstructured":"Scholak, T., Schucher, N., Bahdanau, D.: PICARD: parsing incrementally for constrained auto-regressive decoding from language models. ArXiv abs\/2109.05093 (2021)","DOI":"10.18653\/v1\/2021.emnlp-main.779"},{"key":"12_CR27","doi-asserted-by":"crossref","unstructured":"Sun, W., et al.: Is ChatGPT good at search? Investigating large language models as re-ranking agents. ArXiv abs\/2304.09542 (2023)","DOI":"10.18653\/v1\/2023.emnlp-main.923"},{"key":"12_CR28","doi-asserted-by":"crossref","unstructured":"Sun, Y., et al.: Semantic parsing with syntax- and table-aware SQL generation. ArXiv abs\/1804.08338 (2018)","DOI":"10.18653\/v1\/P18-1034"},{"key":"12_CR29","unstructured":"Vaswani, A., et al.: Attention is all you need. ArXiv abs \/1706.03762 (2023)"},{"key":"12_CR30","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: ACL (2020)","DOI":"10.18653\/v1\/2020.acl-main.677"},{"key":"12_CR31","doi-asserted-by":"crossref","unstructured":"Wang, L., et al.: SimLM: pre-training with representation bottleneck for dense passage retrieval. ArXiv abs\/2207.02578 (2023)","DOI":"10.18653\/v1\/2023.acl-long.125"},{"key":"12_CR32","doi-asserted-by":"crossref","unstructured":"Wang, P., Shi, T., Reddy, C.K.: Text-to-SQL generation for question answering on electronic medical records. ArXiv abs\/1908.01839 (2020)","DOI":"10.1145\/3366423.3380120"},{"key":"12_CR33","doi-asserted-by":"crossref","unstructured":"Yang, J., Gupta, A., Upadhyay, S., He, L., Goel, R., Paul, S.: TableFormer: robust transformer modeling for table-text encoding. ArXiv abs\/2203.00274 (2022)","DOI":"10.18653\/v1\/2022.acl-long.40"},{"key":"12_CR34","doi-asserted-by":"crossref","unstructured":"Yu, T., Li, Z., Zhang, Z., Zhang, R., Radev, D.R.: TypeSQL: knowledge-based typeaware neural text-to-SQL generation. In: NAACL (2018)","DOI":"10.18653\/v1\/N18-2093"},{"key":"12_CR35","doi-asserted-by":"crossref","unstructured":"Yu, T., et al.: Spider: A large-scale human-labeled dataset for complex and cross-domain semantic parsing and text-to-SQL task. In: EMNLP (2018)","DOI":"10.18653\/v1\/D18-1425"},{"key":"12_CR36","doi-asserted-by":"crossref","unstructured":"Zayats, V., Toutanova, K., Ostendorf, M.: Representations for question answering from documents with tables and text. In: EACL (2021)","DOI":"10.18653\/v1\/2021.eacl-main.253"},{"key":"12_CR37","doi-asserted-by":"crossref","unstructured":"Zhong, W., et al.: Reasoning over hybrid chain for table-and-text open domain QA. ArXiv abs\/2201.05880 (2022)","DOI":"10.24963\/ijcai.2022\/629"},{"key":"12_CR38","doi-asserted-by":"crossref","unstructured":"Zhu, F., et al.: TAT-QA: a question answering benchmark on a hybrid of tabular and textual content in finance. In: ACL (2021)","DOI":"10.18653\/v1\/2021.acl-long.254"}],"container-title":["Communications in Computer and Information Science","Engineering Applications of Neural Networks"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-62495-7_12","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,6,21]],"date-time":"2024-06-21T20:21:25Z","timestamp":1719001285000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-62495-7_12"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031624940","9783031624957"],"references-count":38,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-62495-7_12","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"value":"1865-0929","type":"print"},{"value":"1865-0937","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"22 June 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"EANN","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Engineering Applications of Neural Networks","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Corfu","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Greece","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 June 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30 June 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eann2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eannconf.org\/2024\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}