  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.
