Bridging the gap between text-to-SQL research and real-world applications: A unified all-in-one framework for text-to-SQL

Existing text-to-SQL research assumes the availability of gold table when generating SQL queries. It is possible to effectively generate complex and difficult queries by leveraging information from the gold table. However, in real-world scenarios, determining which of the numerous tables in a database should be referenced is challenging. Therefore, existing models reveal a gap in achieving the core objective of practicality in text-to-SQL research. In response, we propose a practical framework that can effectively convert user questions into queries, even in scenarios where reference tables are not provided. By adding a phase to find tables, it can generate queries using only information from questions, mitigating the limitations that arise when restricting reference tables to a single one. We demonstrate that our methods are suitable for practical use in text-to-SQL systems by achieving performances comparable to those of existing models with simple structures.