  Measuring sentence similarity is a key research area nowadays as it allows
machines to better understand human languages. In this paper, we proposed a
Cross-Attention Siamese Network (CATsNet) to carry out the task of learning the
semantic meanings of Chinese sentences and comparing the similarity between two
sentences. This novel model is capable of catching non-local features.
Additionally, we also tried to apply the long short-term memory (LSTM) network
in the model to improve its performance. The experiments were conducted on the
LCQMC dataset and the results showed that our model could achieve a higher
accuracy than previous work.
