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，因为标签构成复杂的层次结构。现有的HTC双编码器方法在带来巨大内存开销的同时仅能实现微弱的性能提升，且其结构编码器严重依赖领域知识。基于此观察，我们致力于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的性能。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，我们以结构熵为指导，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，将编码树中层次感知的信息融入文本表示。除文本编码器外，HiTIN仅包含少量多层感知机与线性变换，大幅节省内存。我们在三个常用数据集上的实验表明，HiTIN相比当前最优（SOTA）方法能够实现更优的测试性能与更少的内存消耗。