%0 Conference Proceedings %T Towards Scalable and Reliable Capsule Networks for Challenging NLP Applications %A Zhao, Wei %A Peng, Haiyun %A Eger, Steffen %A Cambria, Erik %A Yang, Min %Y Korhonen, Anna %Y Traum, David %Y Màrquez, Lluís %S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics %D 2019 %8 July %I Association for Computational Linguistics %C Florence, Italy %F zhao-etal-2019-towards %X 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. %R 10.18653/v1/P19-1150 %U https://aclanthology.org/P19-1150 %U https://doi.org/10.18653/v1/P19-1150 %P 1549-1559