StructCoh: Structured Contrastive Learning for Context-Aware Text Semantic Matching

• URL: http://arxiv.org/abs/2509.02033v1
• Date: Tue, 02 Sep 2025 07:21:36 GMT
• Title: StructCoh: Structured Contrastive Learning for Context-Aware Text Semantic Matching
• Authors: Chao Xue, Ziyuan Gao,
• Abstract summary: StructCoh is a graph-enhanced contrastive learning framework.<n>A hierarchical contrastive objective enforces consistency at multiple granularities.<n>Experiments on three legal document matching benchmarks and academic plagiarism detection datasets demonstrate significant improvements.
• Score: 10.000850856259866
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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