通过可解释性分析提升文本转SQL的准确率
The Interpretability Analysis of the Model Can Bring Improvements to the Text-to-SQL Task
- 结合可解释性与执行引导策略解析WHERE子句
- 在WikiSQL上显著提升条件查询准确率，减少对标注数据依赖
- 适合关注数据库查询鲁棒性与低资源场景的研究者
为提升文本转SQL模型在真实场景中的基础能力与泛化性能，本文将模型可解释性分析与执行引导策略相结合，用于处理SQL查询中WHERE子句的语义解析。进一步引入过滤调整、逻辑关联优化与模型融合，提出CESQL模型，实现条件增强。该模型在代表单表查询任务的WikiSQL数据集上表现优异，显著提升了预测准确性。在预测WHERE子句中的条件值时，不仅降低了对表内条件列数据的依赖，还避免了人工标注训练数据的影响。本研究旨在为复杂查询及现实数据库中不规则数据场景下的处理提供新思路。
原文摘要 · Abstract (English)
To elevate the foundational capabilities and generalization prowess of the text-to-SQL model in real-world applications, we integrate model interpretability analysis with execution-guided strategy for semantic parsing of WHERE clauses in SQL queries. Furthermore, we augment this approach with filtering adjustments, logical correlation refinements, and model fusion, culminating in the design of the CESQL model that facilitates conditional enhancement. Our model excels on the WikiSQL dataset, which is emblematic of single-table database query tasks, markedly boosting the accuracy of prediction outcomes. When predicting conditional values in WHERE clauses, we have not only minimized our dependence on data within the condition columns of tables but also circumvented the impact of manually labeled training data. Our hope is that this endeavor to enhance accuracy in processing basic database queries will offer fresh perspectives for research into handling complex queries and scenarios featuring irregular data in real-world database environments.
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