Light-Weighted CNN for Text Classification

TL;DR

This paper introduces a lightweight CNN architecture using separable convolution for text classification, significantly reducing memory usage while maintaining accuracy.

Contribution

The novel application of separable convolution to text classification CNNs reduces parameters and resource consumption.

Findings

Significant reduction in trainable parameters.

Maintains high accuracy in text classification.

First use of separable convolution in text CNNs.

Abstract

For management, documents are categorized into a specific category, and to do these, most of the organizations use manual labor. In today's automation era, manual efforts on such a task are not justified, and to avoid this, we have so many software out there in the market. However, efficiency and minimal resource consumption is the focal point which is also creating a competition. The categorization of such documents into specified classes by machine provides excellent help. One of categorization technique is text classification using a Convolutional neural network(TextCNN). TextCNN uses multiple sizes of filters, as in the case of the inception layer introduced in Googlenet. The network provides good accuracy but causes high memory consumption due to a large number of trainable parameters. As a solution to this problem, we introduced a whole new architecture based on separable convolution. The idea of separable convolution already exists in the field of image classification but not yet introduces to text classification tasks. With the help of this architecture, we can achieve a drastic reduction in trainable parameters.