  Transformer-based pre-trained language models such as BERT have achieved
remarkable results in Semantic Sentence Matching. However, existing models
still suffer from insufficient ability to capture subtle differences. Minor
noise like word addition, deletion, and modification of sentences may cause
flipped predictions. To alleviate this problem, we propose a novel Dual
Attention Enhanced BERT (DABERT) to enhance the ability of BERT to capture
fine-grained differences in sentence pairs. DABERT comprises (1) Dual Attention
module, which measures soft word matches by introducing a new dual channel
alignment mechanism to model affinity and difference attention. (2) Adaptive
Fusion module, this module uses attention to learn the aggregation of
difference and affinity features, and generates a vector describing the
matching details of sentence pairs. We conduct extensive experiments on
well-studied semantic matching and robustness test datasets, and the
experimental results show the effectiveness of our proposed method.
