论文标题
HPT：层次结构意识提示调整分层文本分类
HPT: Hierarchy-aware Prompt Tuning for Hierarchical Text Classification
论文作者
论文摘要
分层文本分类（HTC）由于其复杂的标签层次结构而是多标签分类的具有挑战性的子任务。最近，验证的语言模型（PLM）通过微调范式在HTC中广泛采用。但是，在此范式中，具有复杂标签层次结构的分类任务与PLM的蒙版语言模型（MLM）预处理任务之间存在巨大差距，因此PLM的潜力无法全面利用。为了弥合差距，在本文中，我们提出了HPT，这是一种从多标签MLM角度处理HTC的层次结构及时调整方法。具体而言，我们构建了一个动态虚拟模板和标签单词，该模板采用软提示的形式融合标签层次结构知识并引入零结合的多标签横熵损失，以协调HTC和MLM的目标。广泛的实验表明，HPT在3个流行的HTC数据集上实现了最先进的性能，并且擅长处理不平衡和低资源情况。我们的代码可在https://github.com/wzh9969/hpt上找到。
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 https://github.com/wzh9969/HPT.
