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

• URL: http://arxiv.org/abs/2208.12711v1
• Date: Fri, 26 Aug 2022 15:11:10 GMT
• Title: SeSQL: Yet Another Large-scale Session-level Chinese Text-to-SQL Dataset
• Authors: Saihao Huang, Lijie Wang, Zhenghua Li, Zeyang Liu, Chenhui Dou, Fukang Yan, Xinyan Xiao, Hua Wu, Min Zhang
• Abstract summary: CHASE contains 2,003 sessions manually constructed from scratch (CHASE-C) and 3,456 sessions translated from English (CHASE-T) We find the two parts are highly discrepant and incompatible as training and evaluation data. In this work, we present Se, yet another large-scale session-level text-to- parsing dataset in Chinese, consisting of 5,028 sessions all manually constructed from scratch.
• Score: 39.78074639729293
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: As the first session-level Chinese dataset, CHASE contains two separate parts, i.e., 2,003 sessions manually constructed from scratch (CHASE-C), and 3,456 sessions translated from English SParC (CHASE-T). We find the two parts are highly discrepant and incompatible as training and evaluation data. In this work, we present SeSQL, yet another large-scale session-level text-to-SQL dataset in Chinese, consisting of 5,028 sessions all manually constructed from scratch. In order to guarantee data quality, we adopt an iterative annotation workflow to facilitate intense and in-time review of previous-round natural language (NL) questions and SQL queries. Moreover, by completing all context-dependent NL questions, we obtain 27,012 context-independent question/SQL pairs, allowing SeSQL to be used as the largest dataset for single-round multi-DB text-to-SQL parsing. We conduct benchmark session-level text-to-SQL parsing experiments on SeSQL by employing three competitive session-level parsers, and present detailed analysis.

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