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Addressing the mismatch between natural language descriptions and thecorresponding SQL queries is a key challenge for text-to-SQL translation. Tobridge this gap, we propose an SQL intermediate representation (IR) calledNatural SQL (NatSQL). Specifically, NatSQL preserves the core functionalitiesof SQL, while it simplifies the queries as follows: (1) dispensing withoperators and keywords such as GROUP BY, HAVING, FROM, JOIN ON, which areusually hard to find counterparts for in the text descriptions; (2) removingthe need for nested subqueries and set operators; and (3) making schema linkingeasier by reducing the required number of schema items. On Spider, achallenging text-to-SQL benchmark that contains complex and nested SQL queries,we demonstrate that NatSQL outperforms other IRs, and significantly improvesthe performance of several previous SOTA models. Furthermore, for existingmodels that do not support executable SQL generation, NatSQL easily enablesthem to generate executable SQL queries, and achieves the new state-of-the-artexecution accuracy.

Natural SQL: Making SQL Easier to Infer from Natural Language Specifications