  Sentence semantic matching requires an agent to determine the semantic
relation between two sentences, where much recent progress has been made by the
advancement of representation learning techniques and inspiration of human
behaviors. Among all these methods, attention mechanism plays an essential role
by selecting important parts effectively. However, current attention methods
either focus on all the important parts in a static way or only select one
important part at one attention step dynamically, which leaves a large space
for further improvement. To this end, in this paper, we design a novel Dynamic
Gaussian Attention Network (DGA-Net) to combine the advantages of current
static and dynamic attention methods. More specifically, we first leverage
pre-trained language model to encode the input sentences and construct semantic
representations from a global perspective. Then, we develop a Dynamic Gaussian
Attention (DGA) to dynamically capture the important parts and corresponding
local contexts from a detailed perspective. Finally, we combine the global
information and detailed local information together to decide the semantic
relation of sentences comprehensively and precisely. Extensive experiments on
two popular sentence semantic matching tasks demonstrate that our proposed
DGA-Net is effective in improving the ability of attention mechanism.
