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:以变异器为基础的预培训模型在语义匹配方面取得了很大的改进,然而,现有模型仍然缺乏捕捉微妙差异的足够能力。 修改、增删对句中的词句可能会使模型难以预测其关系。 为了缓解这一问题,我们提议了一个新的双轨模式模型框架,以提高模型通过分别建模亲和差异语义来识别对句微妙差异的能力。 我们根据双路建模框架设计了双路建模网络(DPM-Net),以识别语义关系。我们还就10个经过良好研究的语义匹配和稳健性测试数据集进行了广泛的实验,实验结果表明,我们拟议的方法在基线上取得了一致的改进。</s>