A Survey on Text-to-SQL Parsing: Concepts, Methods, and Future Directions
Text-to-SQL parsing is an essential and challenging task. The goal of text-to-SQL parsing is to convert a natural language (NL) question to its corresponding structured query language (SQL) based on the evidences provided by relational databases. Early text-to-SQL parsing systems from the database c...
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Main Authors | , , , , , , , , , , , |
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Format | Journal Article |
Language | English |
Published |
29.08.2022
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Subjects | |
Online Access | Get full text |
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Summary: | Text-to-SQL parsing is an essential and challenging task. The goal of
text-to-SQL parsing is to convert a natural language (NL) question to its
corresponding structured query language (SQL) based on the evidences provided
by relational databases. Early text-to-SQL parsing systems from the database
community achieved a noticeable progress with the cost of heavy human
engineering and user interactions with the systems. In recent years, deep
neural networks have significantly advanced this task by neural generation
models, which automatically learn a mapping function from an input NL question
to an output SQL query. Subsequently, the large pre-trained language models
have taken the state-of-the-art of the text-to-SQL parsing task to a new level.
In this survey, we present a comprehensive review on deep learning approaches
for text-to-SQL parsing. First, we introduce the text-to-SQL parsing corpora
which can be categorized as single-turn and multi-turn. Second, we provide a
systematical overview of pre-trained language models and existing methods for
text-to-SQL parsing. Third, we present readers with the challenges faced by
text-to-SQL parsing and explore some potential future directions in this field. |
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DOI: | 10.48550/arxiv.2208.13629 |