The fundamental goal of the Text-to-SQL task is to translate natural language question into SQL query. Current research primarily emphasizes the information coupling between natural language questions and schemas, and significant progress has been made in this area. The natural language questions as the primary task requirements source determines the hardness of correspond SQL queries, the correlation between the two always be ignored. However, when the correlation between questions and queries was decoupled, it may simplify the task. In this paper, we introduce an innovative framework for Text-to-SQL based on decoupling SQL query hardness parsing. This framework decouples the Text-to-SQL task based on query hardness by analyzing questions and schemas, simplifying the multi-hardness task into a single-hardness challenge. This greatly reduces the parsing pressure on the language model. We evaluate our proposed framework and achieve a new state-of-the-art performance of fine-turning methods on Spider dev.

翻译：文本转SQL任务的基本目标是将自然语言问题转化为SQL查询语句。当前研究主要强调自然语言问题与数据库模式之间的信息耦合，并在此方面取得了显著进展。自然语言问题作为任务需求的主要来源，决定了对应SQL查询的难度，但两者之间的关联性常被忽视。然而，将问题与查询之间的关联性解耦，可能会简化该任务。本文提出了一种基于解耦SQL查询难度解析的文本转SQL创新框架。该框架通过分析问题和模式，基于查询难度对文本转SQL任务进行解耦，将多难度任务简化为单一难度挑战，从而大幅降低了语言模型的解析压力。我们对所提框架进行了评估，并在Spider开发集上取得了微调方法的最新最优性能。