  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.
