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    <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.</abstract>
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%0 Conference Proceedings
%T Investigating Capsule Networks with Dynamic Routing for Text Classification
%A Zhao, Wei
%A Ye, Jianbo
%A Yang, Min
%A Lei, Zeyang
%A Zhang, Suofei
%A Zhao, Zhou
%Y Riloff, Ellen
%Y Chiang, David
%Y Hockenmaier, Julia
%Y Tsujii, Jun’ichi
%S Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
%D 2018
%8 oct nov
%I Association for Computational Linguistics
%C Brussels, Belgium
%F zhao-etal-2018-investigating
%X 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.
%R 10.18653/v1/D18-1350
%U https://aclanthology.org/D18-1350/
%U https://doi.org/10.18653/v1/D18-1350
%P 3110-3119

Markdown (Informal)

[Investigating Capsule Networks with Dynamic Routing for Text Classification](https://aclanthology.org/D18-1350/) (Zhao et al., EMNLP 2018)

• Investigating Capsule Networks with Dynamic Routing for Text Classification (Zhao et al., EMNLP 2018)

ACL

• Wei Zhao, Jianbo Ye, Min Yang, Zeyang Lei, Suofei Zhang, and Zhou Zhao. 2018. Investigating Capsule Networks with Dynamic Routing for Text Classification. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 3110–3119, Brussels, Belgium. Association for Computational Linguistics.