  Hierarchical Text Classification (HTC), which aims to predict text labels
organized in hierarchical space, is a significant task lacking in investigation
in natural language processing. Existing methods usually encode the entire
hierarchical structure and fail to construct a robust label-dependent model,
making it hard to make accurate predictions on sparse lower-level labels and
achieving low Macro-F1. In this paper, we propose a novel PAMM-HiA-T5 model for
HTC: a hierarchy-aware T5 model with path-adaptive mask mechanism that not only
builds the knowledge of upper-level labels into low-level ones but also
introduces path dependency information in label prediction. Specifically, we
generate a multi-level sequential label structure to exploit hierarchical
dependency across different levels with Breadth-First Search (BFS) and T5
model. To further improve label dependency prediction within each path, we then
propose an original path-adaptive mask mechanism (PAMM) to identify the label's
path information, eliminating sources of noises from other paths. Comprehensive
experiments on three benchmark datasets show that our novel PAMM-HiA-T5 model
greatly outperforms all state-of-the-art HTC approaches especially in Macro-F1.
The ablation studies show that the improvements mainly come from our innovative
approach instead of T5.
