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The cross-domain text-to-SQL task aims to build a system that can parse userquestions into SQL on complete unseen databases, and the single-domaintext-to-SQL task evaluates the performance on identical databases. Both ofthese setups confront unavoidable difficulties in real-world applications. Tothis end, we introduce the cross-schema text-to-SQL task, where the databasesof evaluation data are different from that in the training data but come fromthe same domain. Furthermore, we present CSS, a large-scale CrosS-SchemaChinese text-to-SQL dataset, to carry on corresponding studies. CSS originallyconsisted of 4,340 question/SQL pairs across 2 databases. In order togeneralize models to different medical systems, we extend CSS and create 19 newdatabases along with 29,280 corresponding dataset examples. Moreover, CSS isalso a large corpus for single-domain Chinese text-to-SQL studies. We presentthe data collection approach and a series of analyses of the data statistics.To show the potential and usefulness of CSS, benchmarking baselines have beenconducted and reported. Our dataset is publicly available at\url{https://huggingface.co/datasets/zhanghanchong/css}.

CSS: A Large-scale Cross-schema Chinese Text-to-SQL Medical Dataset