Hierarchical text classification (HTC) is a challenging subtask of
multi-label classification due to its complex label hierarchy. Recently, the
pretrained language models (PLM)have been widely adopted in HTC through a
fine-tuning paradigm. However, in this paradigm, there exists a huge gap
between the classification tasks with sophisticated label hierarchy and the
masked language model (MLM) pretraining tasks of PLMs and thus the potentials
of PLMs can not be fully tapped. To bridge the gap, in this paper, we propose
HPT, a Hierarchy-aware Prompt Tuning method to handle HTC from a multi-label
MLM perspective. Specifically, we construct a dynamic virtual template and
label words that take the form of soft prompts to fuse the label hierarchy
knowledge and introduce a zero-bounded multi-label cross entropy loss to
harmonize the objectives of HTC and MLM. Extensive experiments show HPT
achieves state-of-the-art performances on 3 popular HTC datasets and is adept
at handling the imbalance and low resource situations. Our code is available at
this https URL.

本研究提出了一种分层感知提示调节方法（HPT）来处理多标签文本分类（HTC），通过构建动态虚拟模板和标记词，融合标记层次结构知识并引入零界多标签交叉熵损失，以提高预训练语言模型在 HTC 领域的性能，实验结果表明 HPT 在三组常用数据集上均取得了最先进的表现，能够有效地处理平衡不足和低资源情况。