Syntax and Relation Enhanced Query Generation for Text to SQL Parsing

Abstract

Text-to-SQL parsing is the task of converting natural language questions into executable SQL queries, a significant branch of semantic parsing, which has gained increasing attention in recent years. This technology lowers the barrier for people to access databases, enhancing the convenience and availability of data. However, the primary challenge for text-to-SQL parsing lies in domain adaptation, which concerns whether the model can be applied to new databases and effectively align natural language questions with the corresponding tables or columns within the database. To address these issues, research has introduced SRSQL (Syntax and Relation-Augmented Query Generation), which incorporates syntax information and predefined relationships into the model, effectively utilizing syntactic dependencies and pattern linking to improve performance. Using a Transformer-based decoder, SRSQL generates SQL queries in the form of Abstract Syntax Trees (AST), significantly boosting prediction accuracy. Experimental results show that SRSQL outperforms comparative models, particularly on challenging benchmarks like Spider and Spider-SYN.