Transformer-based pre-trained models have achieved great improvements in semantic matching. However, existing models still suffer from insufficient ability to capture subtle differences. The modification, addition and deletion of words in sentence pairs may make it difficult for the model to predict their relationship. To alleviate this problem, we propose a novel Dual Path Modeling Framework to enhance the model's ability to perceive subtle differences in sentence pairs by separately modeling affinity and difference semantics. Based on dual-path modeling framework we design the Dual Path Modeling Network (DPM-Net) to recognize semantic relations. And we conduct extensive experiments on 10 well-studied semantic matching and robustness test datasets, and the experimental results show that our proposed method achieves consistent improvements over baselines.

翻译：基于Transformer的预训练模型在语义匹配任务中取得了显著进展。然而，现有模型仍存在捕捉细微差异能力不足的问题。句子对中词语的修改、添加或删除可能导致模型难以准确预测其关系。为缓解这一问题，我们提出了一种新颖的双路径建模框架，通过分别建模亲和语义与差异语义，增强模型对句子对中细微差异的感知能力。基于该双路径建模框架，我们设计了双路径建模网络（DPM-Net）以识别语义关系。我们在10个广泛研究的语义匹配与鲁棒性测试数据集上进行了大量实验，实验结果表明，所提出的方法相较于基线模型取得了持续性的性能提升。