用对比学习融合两种层次分类方法，提升文本分类准确率。
Hierarchical Text Classification Using Contrastive Learning Informed Path Guided Hierarchy
- 通过对比学习联合训练文本与层级路径表示
- 在两个公开数据集上宏平均F1提升0.99%-2.37%
- 适合需要精准层次标签的电商、医疗等场景
层次文本分类（HTC）因能处理复杂标签层级结构而受到关注，已广泛应用于电商、客服和医疗等领域。现有模型或单独编码标签层级并混合文本表示，或在文本编码器中引导层级结构，二者各具优势且互补。本文提出一种基于对比学习的路径引导层级分类方法（HTC-CLIP），通过对比学习同时学习层次感知的文本表示与文本驱动的路径引导层级表示。训练时生成两组类别概率分布，推理时对每类概率进行融合以获得最优表示。实验表明，该方法可有效整合此前两种策略，在两个公开基准数据集上相比现有最优模型，宏平均F1得分提升0.99%至2.37%。
原文摘要 · Abstract (English)
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.
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