Dual Path Modeling for Semantic Matching by Perceiving Subtle Conflicts

• URL: http://arxiv.org/abs/2302.12530v1
• Date: Fri, 24 Feb 2023 09:29:55 GMT
• Title: Dual Path Modeling for Semantic Matching by Perceiving Subtle Conflicts
• Authors: Chao Xue and Di Liang and Sirui Wang and Wei Wu and Jing Zhang
• Abstract summary: Transformer-based pre-trained models have achieved great improvements in semantic matching. Existing models still suffer from insufficient ability to capture subtle differences. We propose a novel Dual Path Modeling Framework to enhance the model's ability to perceive subtle differences.
• Score: 14.563722352134949
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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