A survey of deep learning based text-to-SQL generation

Abstract

Text-to-SQL is an important application of automation software engineering. It is also a research hotspot in the field of semantic parsing. The text-to-SQL task aims to automatically generate the SQL statement according to the natural language description. It allows nonprofessionals to access the database without understanding SQL syntax. With the development of large-scale text-to-SQL datasets and artificial intelligence technologies, the text-to-SQL task is also making great progress. Compared with the traditional text-to-SQL generation, the deep learning-based text-to-SQL has the advantages of high accuracy, flexibility, and iterative learning. In recent years, several studies have focused on SQL generation based on deep learning. This research summarizes existing works from the aspects of text-to-SQL scenarios, datasets, model structures, and evaluation methods.