CSS: A Large-scale Cross-schema Chinese Text-to-SQL Medical Dataset
Hanchong Zhang, Jieyu Li, Lu Chen, Ruisheng Cao, Yunyan Zhang, Yu Huang, Yefeng Zheng, Kai Fun Yu · 2023
The cross-domain text-to-SQL task aims to build a system that can parse user questions into SQL on complete unseen databases, and the single-domain text-to-SQL task evaluates the performance on identical databases.Both of these setups confront unavoidable difficulties in real-world applications.To this end, we introduce the cross-schema text-to-SQL task, where the databases of evaluation data are different from that in the training data but come from the same domain.Furthermore, we present CSS 1 , a large-scale CrosS-Schema Chinese text-to-SQL dataset, to carry on corresponding studies.CSS originally consisted of 4,340 question/SQL pairs across 2 databases.In order to generalize models to different medical systems, we extend CSS and create 19 new databases along with 29,280 corresponding dataset examples.Moreover, CSS is also a large corpus for single-domain Chinese textto-SQL studies.We present the data collection approach and a series of analyses of the data statistics.To show the potential and usefulness of CSS, benchmarking baselines have been conducted and reported.