A Wide Residual Network for Sentiment Classification

Recurrent neural network (RNN) is a popular deep learning model for sentiment classification. Most RNN models benefit from the power of the depth of the deep learning network. However, training a sufficiently deep RNN using one word-embedding vector has a problem of feature reuse. In addition, deeper neural networks are more difficult to train. To overcome these problems, in this paper, we describe a sentiment classification model that combines a wide word embedding network architecture and a residual RNN. Firstly, more than one word-embedding vectors are pre-trained to obtain a larger feature space; Secondly, extend the RNN to be a deeper architecture, then employ a residual learning to ease the deep training of networks. Finally, experimental evaluations are conducted on two benchmarks, the results of the proposed networks demonstrate a good applicability for the sentiment classification problem.