TableQA: a Large-Scale Chinese Text-to-SQL Dataset for Table-Aware SQL Generation

Understand

Parsing natural language to corresponding SQL (NL2SQL) with data driven approaches like deep neural networks attracts much attention in recent years.

• Existing NL2SQL datasets assume that condition values should appear exactly in natural language questions and the queries are answerable given the table.
• However, these assumptions may fail in practical scenarios, because user may use different expressions for the same content in the table, and query information outside the table without the full picture of contents in table.
• Therefore we present TableQA, a large-scale cross-domain Natural Language to SQL dataset in Chinese language consisting 64,891 questions and 20,311 unique SQL queries on over 6,000 tables.