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

• URL: http://arxiv.org/abs/2305.15891v1
• Date: Thu, 25 May 2023 09:44:44 GMT
• Title: CSS: A Large-scale Cross-schema Chinese Text-to-SQL Medical Dataset
• Authors: Hanchong Zhang, Jieyu Li, Lu Chen, Ruisheng Cao, Yunyan Zhang, Yu Huang, Yefeng Zheng, Kai Yu
• Abstract summary: We present a large-scale CrosS-Chinese text-to-hugging dataset to carry on corresponding studies. CSS originally consisted of 4,340/ question pairs across 2 databases. In order to generalize models to different medical systems, we create 19 new databases along with 29,280 corresponding examples.
• Score: 40.43236560948185
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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}.

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