  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.
