  Transformer-based pre-trained models like BERT have achieved great progress
on Semantic Sentence Matching. Meanwhile, dependency prior knowledge has also
shown general benefits in multiple NLP tasks. However, how to efficiently
integrate dependency prior structure into pre-trained models to better model
complex semantic matching relations is still unsettled. In this paper, we
propose the \textbf{D}ependency-Enhanced \textbf{A}daptive \textbf{F}usion
\textbf{A}ttention (\textbf{DAFA}), which explicitly introduces dependency
structure into pre-trained models and adaptively fuses it with semantic
information. Specifically, \textbf{\emph{(i)}} DAFA first proposes a
structure-sensitive paradigm to construct a dependency matrix for calibrating
attention weights. It adopts an adaptive fusion module to integrate the
obtained dependency information and the original semantic signals. Moreover,
DAFA reconstructs the attention calculation flow and provides better
interpretability. By applying it on BERT, our method achieves state-of-the-art
or competitive performance on 10 public datasets, demonstrating the benefits of
adaptively fusing dependency structure in semantic matching task.
