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

Naihao Deng, Yulong Chen, Yue Zhang


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 building natural language interfaces to database systems. The major challenges in text-to-SQL lie in encoding the meaning of natural utterances, decoding to SQL queries, and translating the semantics between these two forms. These challenges 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 this survey can serve as quick access to existing work and motivate future research.
Anthology ID:
2022.coling-1.190
Volume:
Proceedings of the 29th International Conference on Computational Linguistics
Month:
October
Year:
2022
Address:
Gyeongju, Republic of Korea
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
2166–2187
Language:
URL:
https://aclanthology.org/2022.coling-1.190
DOI:
Bibkey:
Cite (ACL):
Naihao Deng, Yulong Chen, and Yue Zhang. 2022. Recent Advances in Text-to-SQL: A Survey of What We Have and What We Expect. In Proceedings of the 29th International Conference on Computational Linguistics, pages 2166–2187, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
Cite (Informal):
Recent Advances in Text-to-SQL: A Survey of What We Have and What We Expect (Deng et al., COLING 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.coling-1.190.pdf
Code
 text-to-sql-survey-coling22/text-to-sql-survey-coling22.github.io
Data
SEDEWikiSQL