Towards Scalable and Reliable Capsule Networks for Challenging NLP Applications
Wei Zhao | Haiyun Peng | Steffen Eger | Erik Cambria | Min Yang
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
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: (i) an agreement score to evaluate the performance of routing processes at instance-level; (ii) an adaptive optimizer to enhance the reliability of routing; (iii) 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.