An Attention-Gated Convolutional Neural Network for Sentence Classification

The classification task of sentences is very challenging because of the limited contextual information that sentences contain. In this paper, we propose an Attention Gated Convolutional Neural Network (AGCNN) for sentence classification, which generates attention weights from the feature's context windows of different sizes by using specialized convolution encoders, to enhance the influence of critical features in predicting the sentence's category. Experimental results demonstrate that our model could achieve a general accuracy improvement highest up to 3.1% (compared with standard CNN models), and gain competitive results over the strong baseline methods on four out of the six tasks. Besides, we propose an activation function named Natural Logarithm rescaled Rectified Linear Unit (NLReLU). Experimental results show that NLReLU could outperform ReLU and performs comparably to other well-known activation functions on AGCNN.