S$^2$SQL: Injecting Syntax to Question-Schema Interaction Graph Encoder for Text-to-SQL Parsers
This work improves text-to-SQL parsing, a key task in semantic parsing for database querying, by enhancing syntax integration, though it is incremental as it builds on existing graph-based methods.
The paper tackled the problem of text-to-SQL parsing by addressing the lack of syntactic modeling in graph-based encoders, proposing S^2SQL to inject question syntax into the encoder and using a decoupling constraint for relational edge embeddings, resulting in state-of-the-art performance on the Spider and Spider-Syn benchmarks.
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.