Towards Scalable and Reliable Capsule Networks for Challenging NLP Applications

TL;DR

This paper proposes scalable and reliable capsule network methods for NLP tasks, introducing new evaluation metrics, optimization techniques, and scalability improvements, validated through experiments on multi-label classification and question answering.

Contribution

It introduces novel techniques including an agreement score, adaptive optimizer, capsule compression, and partial routing to enhance capsule networks for NLP.

Findings

Significant performance improvements over strong competitors.

Best results achieved in low-resource settings.

Enhanced reliability and scalability of capsule networks.

Abstract

Obstacles hindering the development of capsule networks for challenging NLP applications include poor scalability to large output spaces and less reliable routing processes. In this paper, we introduce: 1) an agreement score to evaluate the performance of routing processes at instance level; 2) an adaptive optimizer to enhance the reliability of routing; 3) capsule compression and partial routing to improve the scalability of capsule networks. We validate our approach on two NLP tasks, namely: multi-label text classification and question answering. Experimental results show that our approach considerably improves over strong competitors on both tasks. In addition, we gain the best results in low-resource settings with few training instances.