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As the first session-level Chinese dataset, CHASE contains two separateparts, i.e., 2,003 sessions manually constructed from scratch (CHASE-C), and3,456 sessions translated from English SParC (CHASE-T). We find the two partsare highly discrepant and incompatible as training and evaluation data. In thiswork, we present SeSQL, yet another large-scale session-level text-to-SQLdataset in Chinese, consisting of 5,028 sessions all manually constructed fromscratch. In order to guarantee data quality, we adopt an iterative annotationworkflow to facilitate intense and in-time review of previous-round naturallanguage (NL) questions and SQL queries. Moreover, by completing allcontext-dependent NL questions, we obtain 27,012 context-independentquestion/SQL pairs, allowing SeSQL to be used as the largest dataset forsingle-round multi-DB text-to-SQL parsing. We conduct benchmark session-leveltext-to-SQL parsing experiments on SeSQL by employing three competitivesession-level parsers, and present detailed analysis.

SeSQL: Yet Another Large-scale Session-level Chinese Text-to-SQL Dataset