{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/hitin-hierarchy-aware-tree-isomorphism","title":"HiTIN: Hierarchy-aware Tree Isomorphism Network for Hierarchical Text Classification","arxiv_id":"2305.15182","date":"2023-05-24","proceeding":null,"authors":["He Zhu","Chong Zhang","JunJie Huang","Junran Wu","Ke Xu"],"abstract":"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.","url_abs":"https://arxiv.org/abs/2305.15182v2","url_pdf":"https://arxiv.org/pdf/2305.15182v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"hitin-hierarchy-aware-tree-isomorphism","repo_url":"https://github.com/rooooyy/hitin","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"hierarchical-multi-label-classification","task_name":"Hierarchical Multi-label Classification"},{"task_slug":"multi-label-classification-2","task_name":"MUlTI-LABEL-ClASSIFICATION"},{"task_slug":"multi-label-classification","task_name":"Multi-Label Classification"},{"task_slug":"multi-label-text-classification","task_name":"Multi-Label Text Classification"},{"task_slug":"text-classification","task_name":"Text Classification"},{"task_slug":"text-classification-1","task_name":"text-classification"}],"methods":[{"method_slug":"1x1-convolution","method_name":"1x1 Convolution"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"fpn","method_name":"FPN"},{"method_slug":"htc","method_name":"HTC"},{"method_slug":"rpn","method_name":"RPN"},{"method_slug":"roi-align","method_name":"RoIAlign"},{"method_slug":"test","method_name":"Test"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/hierarchical-multi-label-classification-on-18","task":"Hierarchical Multi-label Classification","dataset":"New York Times Annotated Corpus","model":"HiTIN+BERT","rank_in_archive_order":6,"of":7,"metrics":{"Macro F1":"69.31","Micro F1":"79.65"},"uses_additional_data":false},{"leaderboard":"/sota/hierarchical-multi-label-classification-on-18","task":"Hierarchical Multi-label Classification","dataset":"New York Times Annotated Corpus","model":"HiTIN","rank_in_archive_order":7,"of":7,"metrics":{"Macro F1":"61.09","Micro F1":"75.13"},"uses_additional_data":false},{"leaderboard":"/sota/hierarchical-multi-label-classification-on-17","task":"Hierarchical Multi-label Classification","dataset":"RCV1-v2","model":"HiTIN+BERT","rank_in_archive_order":6,"of":7,"metrics":{"Macro F1":"69.95","Micro F1":"86.71"},"uses_additional_data":false},{"leaderboard":"/sota/hierarchical-multi-label-classification-on-17","task":"Hierarchical Multi-label Classification","dataset":"RCV1-v2","model":"HiTIN","rank_in_archive_order":7,"of":7,"metrics":{"Macro F1":"64.37","Micro F1":"84.81"},"uses_additional_data":false},{"leaderboard":"/sota/hierarchical-multi-label-classification-on-16","task":"Hierarchical Multi-label Classification","dataset":"WOS","model":"HiTIN+BERT","rank_in_archive_order":2,"of":3,"metrics":{"Macro F1":"81.57","Micro F1":"87.19"},"uses_additional_data":false},{"leaderboard":"/sota/hierarchical-multi-label-classification-on-16","task":"Hierarchical Multi-label Classification","dataset":"WOS","model":"HiTIN","rank_in_archive_order":3,"of":3,"metrics":{"Macro F1":"81.11","Micro F1":"86.66"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2305.15182","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.15182"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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