Investigating Capsule Networks with Dynamic Routing for Text Classification
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；Pennsylvania State University（宾夕法尼亚州立大学）；Graduate School at Shenzhen, Tsinghua University（清华大学深圳研究生院）；Nanjing University of Posts and Telecommunications（南京邮电大学）；Zhejiang University（浙江大学）；Tencent（腾讯）
Wei Zhao, Jianbo Ye, Min Yang, Zeyang Lei, Suofei Zhang, Zhou Zhao
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.
Northwestern Polytechnical University（西北工业大学）；Technical University of Darmstadt（达姆施塔特工业大学）；Nanyang Technological University（南洋理工大学）；Pennsylvania State University（宾夕法尼亚州立大学）
Towards Scalable and Reliable Capsule Networks for Challenging NLP Applications
Technische Universität Darmstadt（达姆施塔特工业大学）；Nanyang Technological University（南洋理工大学）；Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）
Joint Slot Filling and Intent Detection via Capsule Neural Networks
University of Illinois at Chicago（伊利诺伊大学芝加哥分校）；Alibaba Group（阿里巴巴集团）；Tencent Medical AI Lab（腾讯医疗人工智能实验室）；Institute for Data Science, Tsinghua University（清华大学数据科学研究院）
