Recent Advances in Text-to-SQL: A Survey of What We Have and What We Expect
It offers a comprehensive overview for researchers and practitioners in NLP and databases, but it is incremental as it synthesizes existing work without new methods or results.
This paper provides a systematic survey of recent advances in text-to-SQL, addressing challenges in encoding natural language, decoding SQL queries, and translating semantics, and discusses future directions to motivate research.
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 that this survey can serve as quick access to existing work and motivate future research.