Dual Prompt Tuning based Contrastive Learning for Hierarchical Text Classification

Hierarchical text classification aims at categorizing texts into multi-tiered tree-like label hierarchy. Existing methods pay more attention to capture hierarchy-aware text feature by exploiting explicit parent-child relationships, while interactions between peer labels are rarely taken into account, resulting in severe label confusions within each layer. In this work, we propose a novel Dual Prompt Tuning (DPT) method, which emphasizes to identify discrimination among peer labels by performing contrastive learning on each hierarchical layer. We design an innovative hand-crafted prompt containing slots for both positive and negative label predictions to cooperate with contrastive learning. In addition, we introduce a label hierarchy self-sensing auxiliary task to ensure cross-layer label consistency. Extensive experiments demonstrate that DPT achieves significant improvements and outperforms the current state-of-the-art methods on BGC and RCV1-V2 benchmark datasets. predict labels that match the hierarchical relation-041