Text classification model based on static routing and grouped capsule network

Abstract:

The use of capsule network in the fields of image and language have progressed tremendously over the past few years, with the extensive research of the capsule network. However, the capsule network has the disadvantages of too many parameters and long training time. The group feedback routing mechanism is a supervised routing strategy called group-routing. The strategy divides the capsules into several groups evenly, and the capsules locally share the conversion weights, thereby reducing routing parameters and computational complexity, and achieving better results in image classification. In this paper, a new text classification model CapsNet-GSR(CapsNet-Grouped capsule based on Static Routing) is proposed based on the capsule grouping method, and the capsule compression and static routing mechanism are also introduced. The model uses capsule grouping to extract local information of the text while reducing parameters. In addition, it uses capsule compression and static routing mechanisms to further improve the quality of the capsule and reduce the number of parameters. The experiment on the 20 news text classification dataset proves that the number of parameters and training time of the proposed model decrease obviously. The experiment results on AG’s news, TREC, and 20 news datasets show that the accuracy of the proposed model is also improved.