  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.
