Performance Evaluation of Caps-Net Based Multitask Learning Architecture for Text Classification
I. Jeena Jacob
Abstract
The classification of the text involving the process of identification and categorization of text is a tedious and a challenging task too. The Capsules Network (Caps-Net) which is a unique architecture with the capability to confiscate the basic attributes comprising the insights of the particular field that could help in bridging the knowledge gap existing between the source and the destination tasks and capability learn more robust representation than the CNN-Convolutional neural networks in the image classification domain is utilized in the paper to classify the text. As the multi-task learning capability enables to part insights between the tasks that are related and enhances data used in training indirectly, the Caps-Net based multi task learning frame work is proposed in the paper. The proposed architecture including the Caps-Net effectively classifies the text and minimizes the interference experienced among the multiple tasks in the multi-task learning. The architecture put forward is evaluated using various text classification dataset ensuring the efficacy of the proffered frame work