  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, 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 text-to-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. Our dataset is publicly available at
\url{https://huggingface.co/datasets/zhanghanchong/css}.
