Enhanced-RCNN: An Efficient Method for Learning Sentence Similarity

Shuang Peng, Hengbin Cui, Niantao Xie, Sujian Li, Jiaxing Zhang, Xiaolong Li

Abstract: Learning sentence similarity is a fundamental research topic and has been explored using various deep learning methods recently. In this paper, we further propose an enhanced recurrent convolutional neural network (Enhanced-RCNN) model for learning sentence similarity. Compared to the state-of-the-art BERT model, the architecture of our proposed model is far less complex. Experimental results show that our similarity learning method outperforms the baselines and achieves the competitive performance on two real-world paraphrase identification datasets.