Densely Connected CNN with Multi-scale Feature Attention for Text Classification

Shiyao Wang, Minlie Huang, Zhidong Deng

Abstract

Text classification is a fundamental problem in natural language processing. As a popular deep learning model, convolutional neural net- work(CNN) has demonstrated great success in this task. However, most existing CNN models apply convolution filters of fixed window size, thereby unable to learn variable n-gram features flexibly. In this paper, we present a densely connected CNN with multi-scale feature attention for text classification. The dense connections build short-cut paths between upstream and downstream convolutional blocks, which enable the model to compose features of larger scale from those of smaller scale, and thus produce variable n-gram features. Furthermore, a multi-scale feature atten- tion is developed to adaptively select multi-scale features for classification. Extensive experiments demonstrate that our model obtains competitive performance against state-of-the-art baselines on six benchmark datasets. Attention visualization further reveals the model’s ability to select proper n-gram features for text classification. Our code is available at: https://github.com/wangs hy31/Densely-Connected-CNN-with-Multiscale-Feature-Attention.git.