S2SQL: Injecting Syntax to Question-Schema Interaction Graph Encoder for Text-to-SQL Parsers
Binyuan Hui, Ruiying Geng, Lihan Wang, Bowen Qin, Yanyang Li, Bowen Li, Jian Sun, Yongbin Li · Findings of the Association for Computational Linguistics: ACL 2022 · 2022
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.