Investigating Capsule Networks with Dynamic Routing for Text Classification

Min Yang, Wei Zhao, Jianbo Ye, Zeyang Lei, Zhou Zhao, Soufei Zhang


Abstract
In this study, we explore capsule networks with dynamic routing for text classification. We propose three strategies to stabilize the dynamic routing process to alleviate the disturbance of some noise capsules which may contain “background” information or have not been successfully trained. A series of experiments are conducted with capsule networks on six text classification benchmarks. Capsule networks achieve state of the art on 4 out of 6 datasets, which shows the effectiveness of capsule networks for text classification. We additionally show that capsule networks exhibit significant improvement when transfer single-label to multi-label text classification over strong baseline methods. To the best of our knowledge, this is the first work that capsule networks have been empirically investigated for text modeling.
Anthology ID:
D18-1350
Volume:
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
Month:
October-November
Year:
2018
Address:
Brussels, Belgium
Venue:
EMNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
3110–3119
Language:
URL:
https://www.aclweb.org/anthology/D18-1350
DOI:
10.18653/v1/D18-1350
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/D18-1350.pdf
Video:
 https://vimeo.com/305947408