Hierarchical multi-attention networks for document classification

Research of document classification is ongoing to employ the attention based-deep learning algorithms and achieves impressive results. Owing to the complexity of the document, classical models, as well as single attention mechanism, fail to meet the demand of high-accuracy classification. This paper proposes a method that classifies the document via the hierarchical multi-attention networks, which describes the document from the word-sentence level and the sentence-document level. Further, different attention strategies are performed on different levels, which enables accurate assigning of the attention weight. Specifically, the soft attention mechanism is applied to the word-sentence level while the CNN-attention to the sentence-document level. Due to the distinctiveness of the model, the proposed method delivers the highest accuracy compared to other state-of-the-art methods. In addition, the attention weight visualization outcomes present the effectiveness of attention mechanism in distinguishing the importance.

Automated summary

This paper introduces a novel method for document classification, applying different attention strategies at multiple levels to achieve high accuracy. Research indicates that this method is more effective compared to other approaches.