用变量替换增强数学公式检索的图对比学习，保持代数结构不变
Structure-Preserving Graph Contrastive Learning for Mathematical Information Retrieval
- 提出变量替换作为数学图的领域专用增广方法
- 在公式检索任务中显著提升准确率，优于通用增广策略
- 适合需要保留数学结构的符号计算与信息检索研究者
本文提出一种面向数学公式搜索的图对比学习（GCL）专用增广技术——变量替换。标准GCL增广方法常扭曲小而高度结构化的数学公式的语义。变量替换则能有效保留核心代数关系与公式结构。我们将该方法应用于经典的基于GCL的检索模型，实验表明，此简单策略相比通用增广方案显著提升检索性能。代码已开源于GitHub。
原文摘要 · Abstract (English)
This paper introduces Variable Substitution as a domain-specific graph augmentation technique for graph contrastive learning (GCL) in the context of searching for mathematical formulas. Standard GCL augmentation techniques often distort the semantic meaning of mathematical formulas, particularly for small and highly structured graphs. Variable Substitution, on the other hand, preserves the core algebraic relationships and formula structure. To demonstrate the effectiveness of our technique, we apply it to a classic GCL-based retrieval model. Experiments show that this straightforward approach significantly improves retrieval performance compared to generic augmentation strategies. We release the code on GitHub.\footnote{https://github.com/lazywulf/formula_ret_aug}.
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