End-to-End Multi-View Networks for Text Classification

Hongyu Guo; Colin Cherry; Jiang Su

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arXiv · 1704.05907

End-to-End Multi-View Networks for Text Classification

Hongyu Guo·Colin Cherry·Jiang Su

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Abstract

We propose a multi-view network for text classification. Our method automatically creates various views of its input text, each taking the form of soft attention weights that distribute the classifier's focus among a set of base features. For a bag-of-words representation, each view focuses on a different subset of the text's words. Aggregating many such views results in a more discriminative and robust representation. Through a novel architecture that both stacks and concatenates views, we produce a network that emphasizes both depth and width, allowing training to converge quickly. Using our multi-view architecture, we establish new state-of-the-art accuracies on two benchmark tasks.

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Hongyu Guo, Colin Cherry, Jiang Su. 2017-04-19. End-to-End Multi-View Networks for Text Classification. https://arxiv.org/abs/1704.05907

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