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

• URL: http://arxiv.org/abs/2109.05153v1
• Date: Sat, 11 Sep 2021 01:53:55 GMT
• Title: Natural SQL: Making SQL Easier to Infer from Natural Language Specifications
• Authors: Yujian Gan and Xinyun Chen and Jinxia Xie and Matthew Purver and John R. Woodward and John Drake and Qiaofu Zhang
• Abstract summary: We propose an SQL intermediate representation called Natural SQL (Nat) On Spider, a challenging text-to- schema benchmark, we demonstrate that Nat outperforms other IRs, and significantly improves the performance of several previous SOTA models. For existing models that do not support executable generation, Nat easily enables them to generate executable queries, and achieves the new state-of-the-art execution accuracy.
• Score: 15.047104267689052
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Addressing the mismatch between natural language descriptions and the corresponding SQL queries is a key challenge for text-to-SQL translation. To bridge this gap, we propose an SQL intermediate representation (IR) called Natural SQL (NatSQL). Specifically, NatSQL preserves the core functionalities of SQL, while it simplifies the queries as follows: (1) dispensing with operators and keywords such as GROUP BY, HAVING, FROM, JOIN ON, which are usually hard to find counterparts for in the text descriptions; (2) removing the need for nested subqueries and set operators; and (3) making schema linking easier by reducing the required number of schema items. On Spider, a challenging text-to-SQL benchmark that contains complex and nested SQL queries, we demonstrate that NatSQL outperforms other IRs, and significantly improves the performance of several previous SOTA models. Furthermore, for existing models that do not support executable SQL generation, NatSQL easily enables them to generate executable SQL queries, and achieves the new state-of-the-art execution accuracy.

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