The limited scale of annotated data constraints existing context-dependent text-to-SQL models because of the complexity of labeling. The data augmentation method is a commonly used method to solve this problem. However, the data generated by current augmentation methods often lack diversity. In this paper, we introduce ConDA, which generates interactive questions and corresponding SQL results. We designed the SQL dialogue state to enhance the data diversity through the state transition. Meanwhile, we also present a filter method to ensure the data quality by a grounding model. Additionally, we utilize a grounding model to identify and filter low-quality questions that mismatch the state information. Experimental results on the SParC and CoSQL datasets show that ConDA boosts the baseline model to achieve an average improvement of $3.3\%$ on complex questions. Moreover, we analyze the augmented data, which reveals that the data generated by ConDA are of high quality in both SQL template hardness and types, turns, and question consistency.

翻译：标注数据的有限规模限制了现有上下文相关的文本到SQL模型，因为标注的复杂性。数据增强方法是解决此问题的常用方法。然而，当前增强方法生成的数据往往缺乏多样性。在本文中，我们引入了ConDA，它能够生成交互式问题及对应的SQL结果。我们设计了SQL对话状态以通过状态转移增强数据多样性。同时，我们提出了一种过滤方法，通过基础模型确保数据质量。此外，我们利用基础模型识别并过滤掉与状态信息不匹配的低质量问题。在SParC和CoSQL数据集上的实验结果表明，ConDA使基线模型在复杂问题上平均提升了$3.3\%$。此外，我们对增强数据进行了分析，揭示了ConDA生成的数据在SQL模板难度、类型、轮次和问题一致性方面均具有高质量。