Very Deep Convolutional Networks for Natural Language Processing in Tensorflow

This is the DenseNet implementation of the paper Do Convolutional Networks need to be Deep for Text Classification ? in Tensorflow. We study in the paper 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%).

Paper:

Hoa T. Le, Christophe Cerisara, Alexandre Denis. Do Convolutional Networks need to be Deep for Text Classification ?. Association for the Advancement of Artificial Intelligence 2018 (AAAI-18) Workshop on Affective Content Analysis. (https://arxiv.org/abs/1707.04108)

@article{DBLP:journals/corr/LeCD17,
  author    = {Hoa T. Le and
               Christophe Cerisara and
               Alexandre Denis},               
  title     = {Do Convolutional Networks need to be Deep for Text Classification ?},  
  journal   = {CoRR},  
  year      = {2017}  
}

Results:

Reference Source Codes: https://github.com/dennybritz/cnn-text-classification-tf