AAAI Publications, Workshops at the Thirty-Second AAAI Conference on Artificial Intelligence

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Do Convolutional Networks Need to Be Deep for Text Classification ?
Hoa T. Le, Christophe Cerisara, Alexandre Denis

Last modified: 2018-06-20


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%).


Convolutional Neural Networks; Text classification; Sentiment Analysis

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