Do Convolutional Networks need to be Deep for Text Classification ?
Computation and Language
2017-07-14 v1
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\%).
Cite
@article{arxiv.1707.04108,
title = {Do Convolutional Networks need to be Deep for Text Classification ?},
author = {Hoa T. Le and Christophe Cerisara and Alexandre Denis},
journal= {arXiv preprint arXiv:1707.04108},
year = {2017}
}