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.Comment: Accepted by ACL'2