Abstract
Hierarchical Text Classification (HTC) has recently gained traction given the ability to handle complex label hierarchy. This has found applications in domains like E- commerce, customer care and medicine industry among other real-world applications. Existing HTC models either encode label hierarchy separately and mix it with text encoding or guide the label hierarchy structure in the text encoder. Both approaches capture different characteristics of label hierarchy and are complementary to each other. In this paper, we propose a Hierarchical Text Classification using Contrastive Learning Informed Path guided hierarchy (HTC-CLIP), which learns hierarchy-aware text representation and text informed path guided hierarchy representation using contrastive learning. During the training of HTC-CLIP, we learn two different sets of class probabilities distributions and during inference, we use the pooled output of both probabilities for each class to get the best of both representations. Our results show that the two previous approaches can be effectively combined into one architecture to achieve improved performance. Tests on two public benchmark datasets showed an improvement of 0.99 - 2.37% in Macro F1 score using HTC-CLIP over the existing state-of-the-art models.
Abstract (translated)
层次文本分类（HTC）最近因其处理复杂标签层级的能力而受到关注，这在电子商务、客户服务和医药行业等众多实际应用中得到了广泛应用。现有的HTC模型要么独立编码标签层级并将其与文本编码混合，要么指导文本编码器中的标签层级结构。这两种方法分别捕捉了标签层级的不同特征，并且互为补充。 本文提出了一种使用对比学习引导路径的层次文本分类（HTC-CLIP）方法，该方法利用对比学习来学习层次感知的文本表示和基于文本的路径引导层次表示。在HTC-CLIP的训练过程中，我们学习两组不同的类别概率分布，并在推理阶段结合这两种概率分布的结果以获得最佳表现。 实验结果表明，将先前提出的两种方法有效整合到一个架构中可以实现性能提升。在两个公开基准数据集上的测试显示，与现有最先进的模型相比，HTC-CLIP的Macro F1得分提高了0.99%至2.37%。
URL
https://arxiv.org/abs/2506.04381
