Data augmentation has attracted a lot of research attention in the deep
learning era for its ability in alleviating data sparseness. The lack of
labeled data for unseen evaluation databases is exactly the major challenge for
cross-domain text-to-SQL parsing. Previous works either require human
intervention to guarantee the quality of generated data, or fail to handle
complex SQL queries. This paper presents a simple yet effective data
augmentation framework. First, given a database, we automatically produce a
large number of SQL queries based on an abstract syntax tree grammar. For
better distribution matching, we require that at least 80% of SQL patterns in
the training data are covered by generated queries. Second, we propose a
hierarchical SQL-to-question generation model to obtain high-quality natural
language questions, which is the major contribution of this work. Finally, we
design a simple sampling strategy that can greatly improve training efficiency
given large amounts of generated data. Experiments on three cross-domain
datasets, i.e., WikiSQL and Spider in English, and DuSQL in Chinese, show that
our proposed data augmentation framework can consistently improve performance
over strong baselines, and the hierarchical generation component is the key for
the improvement.

该论文通过设计一个简单而有效的数据增强框架，提出了一种层次化的 SQL 转自然语言问题生成模型，以及一种简单的采样策略来显著提高训练效率。在三个跨领域数据集上的实验表明，其方法可以一贯地改善表现，并且层次化生成组件是改善的关键。