Schema linking -- the process of aligning natural language questions with database schema elements -- is a critical yet underexplored component of Text-to-SQL systems. While recent methods have focused primarily on improving SQL generation, they often neglect the retrieval of relevant schema elements, which can lead to hallucinations and execution failures. In this work, we propose a context-aware bidirectional schema retrieval framework that treats schema linking as a standalone problem. Our approach combines two complementary strategies: table-first retrieval followed by column selection, and column-first retrieval followed by table selection. It is further augmented with techniques such as question decomposition, keyword extraction, and keyphrase extraction. Through comprehensive evaluations on challenging benchmarks such as BIRD and Spider, we demonstrate that our method significantly improves schema recall while reducing false positives. Moreover, SQL generation using our retrieved schema consistently outperforms full-schema baselines and closely approaches oracle performance, all without requiring query refinement. Notably, our method narrows the performance gap between full and perfect schema settings by 50\%. Our findings highlight schema linking as a powerful lever for enhancing Text-to-SQL accuracy and efficiency.

翻译：模式链接——将自然语言问题与数据库模式元素对齐的过程——是文本到SQL系统中关键但尚未充分探索的组成部分。尽管近期方法主要聚焦于改进SQL生成，它们往往忽略了相关模式元素的检索，这可能导致幻觉与执行失败。在本工作中，我们提出了一种上下文感知的双向模式检索框架，将模式链接视为一个独立问题。我们的方法结合了两种互补策略：先进行表优先检索再进行列选择，以及先进行列优先检索再进行表选择。该方法进一步通过问题分解、关键词提取和关键短语提取等技术进行增强。通过在BIRD和Spider等具有挑战性的基准测试上进行全面评估，我们证明我们的方法显著提高了模式召回率，同时降低了误报率。此外，使用我们检索到的模式进行SQL生成，其性能始终优于全模式基线，并接近理想性能，且无需查询优化。值得注意的是，我们的方法将完整模式与理想模式设置之间的性能差距缩小了50%。我们的研究结果突显了模式链接作为提升文本到SQL准确性与效率的有力杠杆。