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.

本文提出了一种注入语法到问 - Schema 图编码器中用于 Text-to-SQL 解析器的方法 ——S$^2$SQL，它有效地利用了问题中的句法依赖信息来提高性能，并采用解耦约束来诱导多样化的关系边缘嵌入，实验结果在 Spider 和鲁棒性设置 Spider-Syn 上均表现优于所有现有方法，使性能在 Spider 排行榜上名列前茅。