In this paper, we propose an effective model for the similarity metrics of English sentences. In the model, we first make use of word embedding and convolutional neural network (CNN) to produce a sentence vector and then leverage the information of the sentence vector pair to calculate the score of sentence similarity. Considering the case of long-range semantic dependencies between words, we propose a novel method transforming word embeddings to construct the three-dimensional sentence feature tensor. In addition, we incorporate the k-max pooling into the convolutional neural network to adapt to variable lengths of input sentences. The proposed model requires no external resource such as WordNet and parse tree. Meanwhile, it consumes very little time for training. Finally, we carried out extensive simulations to evaluate the performance of our model compared with other state-of-the-art works. Experimental results on SemEval 2014 task (SICK test corpus) indicated that our model can achieve a good performance in the terms of Pearson correlation coefficient, Spearman correlation coefficient, and mean squared errors. Furthermore, experimental results on Microsoft research paraphrase identification (MSRP) indicated that our model can achieve an excellent performance in the terms of F1 and Accuracy.
