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：文到SQL任务的基本目标是将自然语言问题转换为SQL查询。当前研究主要强调自然语言问题与模式之间的信息耦合，且在该领域已取得显著进展。自然语言问题作为主要任务需求来源决定了对应SQL查询的难度，但两者间的关联性始终被忽略。然而，当问题与查询之间的关联被解耦后，可能会简化任务。本文提出一种基于解耦SQL查询难度解析的创新性文到SQL框架。该框架通过分析问题和模式，基于查询难度解耦文到SQL任务，将多难度任务简化为单难度挑战，从而大幅降低语言模型的解析压力。我们评估了所提出的框架，并在Spider开发集上取得了微调方法的最新最佳性能。