Do Convolutional Networks need to be Deep for Text Classification ?

Hoa T. Le; Christophe Cerisara; Alexandre Denis

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arXiv · 1707.04108

Do Convolutional Networks need to be Deep for Text Classification ?

Hoa T. Le·Christophe Cerisara·Alexandre Denis

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Abstract

We study in this work the importance of depth in convolutional models for text classification, either when character or word inputs are considered. We show on 5 standard text classification and sentiment analysis tasks that deep models indeed give better performances than shallow networks when the text input is represented as a sequence of characters. However, a simple shallow-and-wide network outperforms deep models such as DenseNet with word inputs. Our shallow word model further establishes new state-of-the-art performances on two datasets: Yelp Binary (95.9\%) and Yelp Full (64.9\%).

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Hoa T. Le, Christophe Cerisara, Alexandre Denis. 2017-07-13. Do Convolutional Networks need to be Deep for Text Classification ?. https://arxiv.org/abs/1707.04108

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