This work focuses on improving the foundational capabilities and generalization of text-to-SQL models used in real-world applications by combining model interpretability analysis with an execution-guided strategy specifically for semantic parsing of WHERE clauses in SQL queries. The CESQL model further incorporates filtering adjustments, logical correlation refinements, and model fusion to enable conditional enhancements. These innovations lead to superior performance on the WikiSQL dataset, which represents single-table database query tasks, by markedly boosting prediction accuracy. Notably, the model reduces dependence on data from condition columns and avoids the need for manually labeled training data, which are common challenges in text-to-SQL tasks.
The implications of this research are significant for advancing the handling of basic database queries, providing a foundation for tackling more complex queries and irregular data scenarios in real-world databases. By improving interpretability and execution guidance, the CESQL model offers a pathway to more robust and generalizable semantic parsing systems. This could facilitate broader adoption of text-to-SQL technologies in practical applications where data irregularities and limited annotations are prevalent. Future research may build on these insights to further enhance model accuracy and adaptability in diverse database environments.
👉 Pročitaj original: arXiv AI Papers
