  Self Normalizing Neural Networks(SNN) proposed on Feed Forward Neural
Networks(FNN) outperform regular FNN architectures in various machine learning
tasks. Particularly in the domain of Computer Vision, the activation function
Scaled Exponential Linear Units (SELU) proposed for SNNs, perform better than
other non linear activations such as ReLU. The goal of SNN is to produce a
normalized output for a normalized input. Established neural network
architectures like feed forward networks and Convolutional Neural Networks(CNN)
lack the intrinsic nature of normalizing outputs. Hence, requiring additional
layers such as Batch Normalization. Despite the success of SNNs, their
characteristic features on other network architectures like CNN haven't been
explored, especially in the domain of Natural Language Processing. In this
paper we aim to show the effectiveness of proposed, Self Normalizing
Convolutional Neural Networks(SCNN) on text classification. We analyze their
performance with the standard CNN architecture used on several text
classification datasets. Our experiments demonstrate that SCNN achieves
comparable results to standard CNN model with significantly fewer parameters.
Furthermore it also outperforms CNN with equal number of parameters.

Computation and Language