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We present a neural approach called IRNet for complex and cross-domainText-to-SQL. IRNet aims to address two challenges: 1) the mismatch betweenintents expressed in natural language (NL) and the implementation details inSQL; 2) the challenge in predicting columns caused by the large number ofout-of-domain words. Instead of end-to-end synthesizing a SQL query, IRNetdecomposes the synthesis process into three phases. In the first phase, IRNetperforms a schema linking over a question and a database schema. Then, IRNetadopts a grammar-based neural model to synthesize a SemQL query which is anintermediate representation that we design to bridge NL and SQL. Finally, IRNetdeterministically infers a SQL query from the synthesized SemQL query withdomain knowledge. On the challenging Text-to-SQL benchmark Spider, IRNetachieves 46.7% accuracy, obtaining 19.5% absolute improvement over previousstate-of-the-art approaches. At the time of writing, IRNet achieves the firstposition on the Spider leaderboard.

Towards Complex Text-to-SQL in Cross-Domain Database with Intermediate Representation