  The task of converting a natural language question into an executable SQL
query, known as text-to-SQL, is an important branch of semantic parsing. The
state-of-the-art graph-based encoder has been successfully used in this task
but does not model the question syntax well. In this paper, we propose
S$^2$SQL, injecting Syntax to question-Schema graph encoder for Text-to-SQL
parsers, which effectively leverages the syntactic dependency information of
questions in text-to-SQL to improve the performance. We also employ the
decoupling constraint to induce diverse relational edge embedding, which
further improves the network's performance. Experiments on the Spider and
robustness setting Spider-Syn demonstrate that the proposed approach
outperforms all existing methods when pre-training models are used, resulting
in a performance ranks first on the Spider leaderboard.
