通过图结构对比学习，提升法律文本语义匹配精度
StructCoh: Structured Contrastive Learning for Context-Aware Text Semantic Matching
- 构建依赖与主题双图，用图网络传递结构特征
- 多粒度对比学习使核心语义单元和文档结构对齐
- 在法律条文匹配上提升6.2%准确率，适合法律与抄袭检测
文本语义匹配需要精细理解结构关系与细微语义差异。尽管预训练语言模型擅长捕捉词级交互，但常忽略层次化结构模式且难以区分细微语义。本文提出 StructCoh，一种图增强的对比学习框架，协同融合结构推理与表示空间优化。其关键创新包括：(1) 双图编码器通过依存句法分析与主题建模构建语义图，并利用图同构网络在句法依赖与跨文档概念节点间传播结构特征；(2) 分层对比目标在多个粒度上强制一致性：节点级对比正则化保留核心语义单元，图感知对比学习通过显式与隐式负样本策略对齐跨文档结构语义。在三个法律文书匹配基准和学术抄袭检测数据集上的实验表明，该方法显著优于现有最先进方法。特别地，StructCoh 在法律条文匹配任务中达到 86.7% 的 F1 得分，较之前方法提升 6.2% 绝对值。
原文摘要 · Abstract (English)
Text semantic matching requires nuanced understanding of both structural relationships and fine-grained semantic distinctions. While pre-trained language models excel at capturing token-level interactions, they often overlook hierarchical structural patterns and struggle with subtle semantic discrimination. In this paper, we proposed StructCoh, a graph-enhanced contrastive learning framework that synergistically combines structural reasoning with representation space optimization. Our approach features two key innovations: (1) A dual-graph encoder constructs semantic graphs via dependency parsing and topic modeling, then employs graph isomorphism networks to propagate structural features across syntactic dependencies and cross-document concept nodes. (2) A hierarchical contrastive objective enforces consistency at multiple granularities: node-level contrastive regularization preserves core semantic units, while graph-aware contrastive learning aligns inter-document structural semantics through both explicit and implicit negative sampling strategies. Experiments on three legal document matching benchmarks and academic plagiarism detection datasets demonstrate significant improvements over state-of-the-art methods. Notably, StructCoh achieves 86.7% F1-score (+6.2% absolute gain) on legal statute matching by effectively identifying argument structure similarities.
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