Decoupling SQL query hardness parsing for text-to-SQL

The fundamental goal of the Text-to-SQL task is to translate natural language question into SQL query. Current research, which has made significant progress, primarily emphasizes the information coupling between natural language questions and schemas. However, the correlation between natural language questions and SQL queries is often overlooked. Decoupling this correlation may simplify the task. In this paper, we introduce an innovative framework based on Decoupling SQL Query Hardness Parsing for Text-to-SQL. The framework decouples the Text-to-SQL task based on query hardness by analyzing natural language questions and highly relevant schemas. It introduces a distributed structure to simplify multi-hardness task into single-hardness task, thereby significantly reducing the parsing burden on language models. We have evaluated our proposed framework and achieved anew state-of-the-art performance in the methods with model scales less than 3B on the Spider 1.0 dev set. Our code is shown at https://github.com/JarvenYi/DQHP.