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The task of converting a natural language question into an executable SQLquery, known as text-to-SQL, is an important branch of semantic parsing. Thestate-of-the-art graph-based encoder has been successfully used in this taskbut does not model the question syntax well. In this paper, we proposeS$^2$SQL, injecting Syntax to question-Schema graph encoder for Text-to-SQLparsers, which effectively leverages the syntactic dependency information ofquestions in text-to-SQL to improve the performance. We also employ thedecoupling constraint to induce diverse relational edge embedding, whichfurther improves the network's performance. Experiments on the Spider androbustness setting Spider-Syn demonstrate that the proposed approachoutperforms all existing methods when pre-training models are used, resultingin a performance ranks first on the Spider leaderboard.

S$^2$SQL: Injecting Syntax to Question-Schema Interaction Graph Encoder for Text-to-SQL Parsers