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.

本文介绍了一种名为 ConDA 的数据扩充方法，通过交互式问题和相应的 SQL 结果生成数据，利用 SQL 对话状态增强数据多样性，同时利用基础模型确保数据质量。实验结果表明，ConDA 使基础模型的性能平均提升了 3.3％，且生成的数据质量较高。