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Recently, novel text-to-SQL systems are adopting deep learning methods with very promising results. At the same time, several challenges remain open making this area an active and flourishing field of research and development. To make real progress in building text-to-SQL systems, we need to de-mystify what has been done, understand how and when each approach can be used, and, finally, identify the research challenges ahead of us. The purpose of this survey is to present a detailed taxonomy of neural text-to-SQL systems that will enable a deeper study of all the parts of such a system. This taxonomy will allow us to make a better comparison between different approaches, as well as highlight specific challenges in each step of the process, thus enabling researchers to better strategise their quest towards the \u201choly grail\u201d of database accessibility.<\/jats:p>","DOI":"10.1007\/s00778-022-00776-8","type":"journal-article","created":{"date-parts":[[2023,1,23]],"date-time":"2023-01-23T04:16:44Z","timestamp":1674447404000},"page":"905-936","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":147,"title":["A survey on deep learning approaches for text-to-SQL"],"prefix":"10.1007","volume":"32","author":[{"given":"George","family":"Katsogiannis-Meimarakis","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Georgia","family":"Koutrika","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,1,23]]},"reference":[{"key":"776_CR1","doi-asserted-by":"publisher","first-page":"14927","DOI":"10.1109\/ACCESS.2022.3147586","volume":"10","author":"S Abbas","year":"2022","unstructured":"Abbas, S., Khan, M.U., Lee, S.U.-J., Abbas, A., Bashir, A.K.: A review of nlidb with deep learning: findings, challenges and open issues. 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