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

Chinese Sentences Similarity via Cross-Attention Based Siamese Network