Next-Generation Neural Networks: Capsule Networks With Routing-by-Agreement for Text Classification

Abstract

These days, neural networks constantly prove their high capacity for nearly every application case and are considered as key technology for learning systems. However, neural networks need to continuously evolve for managing new arising challenges like increasing task complexity, explainability of decision making processes, expanded problem domains, providing resilient and robust systems etc. One possible enhancement of traditional neural networks constitutes the innovative Capsule Network (CapsNet) technology, which combines the expressiveness of distributed entity representations with an intelligent and interpretable signal propagation, named as routing-by-agreement. Since CapsNets represent a relatively young acquirement, further research is essential for gaining profound knowledge about CapsNet theory and best practices for diverse application areas. This paper wants to contribute to the progress of CapsNets for the task of text classification. For this purpose, various research questions about this technology get formulated and experimentally answered with the aid of six selected datasets. In addition, this paper serves as a possible starting point for researchers as well as for practitioners to deal with CapsNets in the text domain, by supplying a survey about its theory, text classification basics and the combination of both areas. The analysis results empirically prove the robustness of CapsNets with routing-by-agreement for a wide spectrum of net architectures, datasets and text classification tasks. Hence, CapsNets can be viewed as a next-generation neural network technology, which offers high potential as text classification method and should be topic of future research.

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