Towards Scalable and Reliable Capsule Networks for Challenging NLP Applications

Wei Zhao, Haiyun Peng, Steffen Eger, Erik Cambria, Min Yang


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: (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.
Anthology ID:
P19-1150
Volume:
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2019
Address:
Florence, Italy
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1549–1559
Language:
URL:
https://www.aclweb.org/anthology/P19-1150
DOI:
10.18653/v1/P19-1150
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PDF:
http://aclanthology.lst.uni-saarland.de/P19-1150.pdf