Hierarchical Attentional Hybrid Neural Networks for Document Classification

By Jader Abreu, Luis Fred, David Macêdo, Cleber Zanchettin. 2019

39 citations recorded by the source corpus.

Document classification is a challenging task with important applications. The deep learning approaches to the problem have gained much attention recently. Despite the progress, the proposed models do not incorporate the knowledge of the document structure in the architecture efficiently and not take into account the contexting importance of words and sentences. In this paper, we propose a new approach based on a combination of convolutional neural networks, gated recurrent units, and attention mechanisms for document classification tasks. We use of convolution layers varying window sizes to extract more meaningful, generalizable and abstract features by the hierarchical representation. The proposed method in improves the results of the current attention-based approaches for document classification.

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1. Wook-Shin Han
2. Byeong-Hoon So
3. Hongrae Lee

Citations in the corpus

• Natural language to SQL: Where are we today? (2020, background): “The attention encoder passes all the hidden states to the decoder instead of only the last hidden state [1, 27].”