Hierarchical text classification (HTC) is a challenging subtask of multi-label classification as the labels form a complex hierarchical structure. Existing dual-encoder methods in HTC achieve weak performance gains with huge memory overheads and their structure encoders heavily rely on domain knowledge. Under such observation, we tend to investigate the feasibility of a memory-friendly model with strong generalization capability that could boost the performance of HTC without prior statistics or label semantics. In this paper, we propose Hierarchy-aware Tree Isomorphism Network (HiTIN) to enhance the text representations with only syntactic information of the label hierarchy. Specifically, we convert the label hierarchy into an unweighted tree structure, termed coding tree, with the guidance of structural entropy. Then we design a structure encoder to incorporate hierarchy-aware information in the coding tree into text representations. Besides the text encoder, HiTIN only contains a few multi-layer perceptions and linear transformations, which greatly saves memory. We conduct experiments on three commonly used datasets and the results demonstrate that HiTIN could achieve better test performance and less memory consumption than state-of-the-art (SOTA) methods.

翻译：层次文本分类（HTC）是多标签分类中一项具有挑战性的子任务，因为标签形成复杂的层次结构。现有HTC中的双编码器方法以巨大的内存开销为代价实现了微弱的性能提升，且其结构编码器严重依赖领域知识。基于此发现，我们试图探索一种具有强泛化能力且内存友好的模型，该模型无需先验统计信息或标签语义即可提升HTC性能。本文提出层次感知树同构网络（HiTIN），仅利用标签层次的句法信息增强文本表示。具体而言，我们借助结构熵将标签层次转化为无权重树结构（称为编码树），随后设计结构编码器将编码树中的层次感知信息融入文本表示。除文本编码器外，HiTIN仅包含少量多层感知机与线性变换，极大节省了内存。我们在三个常用数据集上开展实验，结果表明HiTIN能以更低内存消耗取得优于现有最先进（SOTA）方法的测试性能。