Recent Advances in Text-to-SQL: A Survey of What We Have and What We Expect

Proceedings of the 29th International Conference on Computational Linguistics, pages 2166–2187 October 12–17, 2022. 2166 Recent Advances in Text-to-SQL: A Survey of What We Have and What We Expect Naihao Deng University of Michigan dnaihao@umich.edu Yulong Chen Westlake University yulongchen1010@gmail.com Yue Zhang Westlake University yue.zhang@wias.org.cn Abstract Text-to-SQL has attracted attention from both the natural language processing and database communities because of its ability to convert the semantics in natural language into SQL queries and its practical application in build- ing natural language interfaces to database sys- tems. The major challenges in text-to-SQL lie in encoding the meaning of natural utterances, decoding to SQL queries, and translating the se- mantics between these two forms. These chal- lenges have been addressed to different extents by the recent advances. However, there is still a lack of comprehensive surveys for this task. To this end, we review recent progress on text- to-SQL for datasets, methods, and evaluation and provide this systematic survey, addressing the aforementioned challenges and discussing potential future directions. We hope that this survey can serve as quick access to existing work and motivate future research. 1 1 Introduction The task of text-to-SQL is to convert natural ut- terances into SQL queries (Zhong et al., 2017; Yu et al., 2018c). Figure 1 shows an example. Given a user utterance “What are the major cities in the state of Kansas?”, the system outputs a correspond- ing SQL that can be used for retrieving the answer from a database. It builds a natural language in- terface to the database (NLIDB) to help lay users access information in the database (Popescu et al., 2003; Li and Jagadish, 2014), inspiring research in human-computer interaction (Elgohary et al., 2020). Because the SQL query can be regarded as a semantic representation (Guo et al., 2020), text- to-SQL is also a representative task in semantic parsing, helping downstream applications such as question answering (Wang et al., 2020d). Thus, text-to-SQL has attracted researchers in the natural 1The Github Link for this survey is: https:// github.com/text-to-sql-survey-coling22/ text-to-sql-survey-coling22.github.io. SELECT T1.CITY_NAME FROM CITY AS T1 WHERE T1.POPULATION > 150000 AND T1.STATE_NAME = "Kansas" ; What are the major cities in the state of Kansas? Model Database End User ? Figure 1: The framework for text-to-SQL systems. Given the database schema and user utterance, the sys- tem outputs a corresponding SQL query to query the database system for the result. Appendix B gives more text-to-SQL examples. language processing (NLP) and the database (DB) community for decades (Codd, 1970; Hemphill et al., 1990; Dahl et al., 1994; Zelle and Mooney, 1996; Popescu et al., 2003; Bertomeu et al., 2006; Wang et al., 2020a; Scholak et al., 2021b). The challenges in text-to-SQL lie within three aspects: (1) extracting the meaning of natural ut- terances (encoding); (2) transforming the extracted meaning into another expression which is pragmat- ically equivalent to the NL meaning (translating) and; (3) producing the corresponding SQL queries (decoding). A wide range of methods has been in- vestigated to address the technical challenges, from …

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