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Hierarchical Text Classification (HTC), which aims to predict text labelsorganized in hierarchical space, is a significant task lacking in investigationin natural language processing. Existing methods usually encode the entirehierarchical structure and fail to construct a robust label-dependent model,making it hard to make accurate predictions on sparse lower-level labels andachieving low Macro-F1. In this paper, we propose a novel PAMM-HiA-T5 model forHTC: a hierarchy-aware T5 model with path-adaptive mask mechanism that not onlybuilds the knowledge of upper-level labels into low-level ones but alsointroduces path dependency information in label prediction. Specifically, wegenerate a multi-level sequential label structure to exploit hierarchicaldependency across different levels with Breadth-First Search (BFS) and T5model. To further improve label dependency prediction within each path, we thenpropose an original path-adaptive mask mechanism (PAMM) to identify the label'spath information, eliminating sources of noises from other paths. Comprehensiveexperiments on three benchmark datasets show that our novel PAMM-HiA-T5 modelgreatly outperforms all state-of-the-art HTC approaches especially in Macro-F1.The ablation studies show that the improvements mainly come from our innovativeapproach instead of T5.

Hierarchy-Aware T5 with Path-Adaptive Mask Mechanism for Hierarchical Text Classification