The task of text-to-SQL is to convert a natural language question to its corresponding SQL query in the context of relational tables. Existing text-to-SQL parsers generate a "plausible" SQL query for an arbitrary user question, thereby failing to correctly handle problematic user questions. To formalize this problem, we conduct a preliminary study on the observed ambiguous and unanswerable cases in text-to-SQL and summarize them into 6 feature categories. Correspondingly, we identify the causes behind each category and propose requirements for handling ambiguous and unanswerable questions. Following this study, we propose a simple yet effective counterfactual example generation approach for the automatic generation of ambiguous and unanswerable text-to-SQL examples. Furthermore, we propose a weakly supervised model DTE (Detecting-Then-Explaining) for error detection, localization, and explanation. Experimental results show that our model achieves the best result on both real-world examples and generated examples compared with various baselines. We will release data and code for future research.

本文旨在研究文本到SQL的自然语言处理问题中的有歧义问题和无法回答问题，并总结为6个特征类别。其次，提出了一个简单而高效的计数事实例子生成方法，用于自动生成有歧义和无法回答的文本到SQL实例。进一步提出了一个基于弱监督模型DTE (Detecting-Then-Explaining) ，用于错误检测、定位和解释。实验结果表明，该模型在真实世界的例子和生成的例子上比各种基线模型都要好。

处理模糊和无法回答的问题：文本到SQL