Label-Correction Capsule Network for Hierarchical Text Classification

Hierarchical Text Classification (HTC) aims to predict the category of a document in a given label hierarchy. Considering a parent-child relationship among labels at different levels, previous works mainly leverage the parent-level label information to guide the child-level classification and achieve promising results. However, they still suffer from two drawbacks: (1) insufficient for distinguishing similar labels at the same level; (2) fail to consider the error propagation problem caused by the incorrect parent-level predictions. For this reason, we first propose a hierarchical capsule network for the HTC task, due to the ability of capsules to distinguish similar categories. To ease the error propagation problem, we further devise two novel mechanisms in the proposed hierarchical capsule framework, i.e., Label Injection and Label Re-Routing, to enhance the tolerance of the model to the incorrect parent-level predictions. Experiments on two widely used datasets prove that our model achieves competitive performance. The ablation study further demonstrates the scalability of Label Injection and Label Re-Routing.