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Text-to-SQL is the problem of converting a user question into an SQL query,when the question and database are given. In this paper, we present a neuralnetwork approach called RYANSQL (Recursively Yielding Annotation Network forSQL) to solve complex Text-to-SQL tasks for cross-domain databases. State-mentPosition Code (SPC) is defined to trans-form a nested SQL query into a set ofnon-nested SELECT statements; a sketch-based slot filling approach is proposedto synthesize each SELECT statement for its corresponding SPC. Additionally,two input manipulation methods are presented to improve generation performancefurther. RYANSQL achieved 58.2% accuracy on the challenging Spider benchmark,which is a 3.2%p improvement over previous state-of-the-art approaches. At thetime of writing, RYANSQL achieves the first position on the Spider leaderboard.

RYANSQL: Recursively Applying Sketch-based Slot Fillings for Complex Text-to-SQL in Cross-Domain Databases