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        <title>Hierarchy-aware Biased Bound Margin Loss Function for Hierarchical Text Classification</title>
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    <abstract>Hierarchical text classification (HTC) is a challenging problem with two key issues: utilizing structural information and mitigating label imbalance. Recently, the unit-based approach generating unit-based feature representations has outperformed the global approach focusing on a global feature representation. Nevertheless, unit-based models using BCE and ZLPR losses still face static thresholding and label imbalance challenges. Those challenges become more critical in large-scale hierarchies. This paper introduces a novel hierarchy-aware loss function for unit-based HTC models: Hierarchy-aware Biased Bound Margin (HBM) loss. HBM integrates learnable bounds, biases, and a margin to address static thresholding and mitigate label imbalance adaptively. Experimental results on benchmark datasets demonstrate the superior performance of HBM compared to competitive HTC models.</abstract>
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%0 Conference Proceedings
%T Hierarchy-aware Biased Bound Margin Loss Function for Hierarchical Text Classification
%A Kim, Gibaeg
%A Im, SangHun
%A Oh, Heung-Seon
%Y Ku, Lun-Wei
%Y Martins, Andre
%Y Srikumar, Vivek
%S Findings of the Association for Computational Linguistics ACL 2024
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand and virtual meeting
%F kim-etal-2024-hierarchy
%X Hierarchical text classification (HTC) is a challenging problem with two key issues: utilizing structural information and mitigating label imbalance. Recently, the unit-based approach generating unit-based feature representations has outperformed the global approach focusing on a global feature representation. Nevertheless, unit-based models using BCE and ZLPR losses still face static thresholding and label imbalance challenges. Those challenges become more critical in large-scale hierarchies. This paper introduces a novel hierarchy-aware loss function for unit-based HTC models: Hierarchy-aware Biased Bound Margin (HBM) loss. HBM integrates learnable bounds, biases, and a margin to address static thresholding and mitigate label imbalance adaptively. Experimental results on benchmark datasets demonstrate the superior performance of HBM compared to competitive HTC models.
%U https://aclanthology.org/2024.findings-acl.457
%P 7672-7682

Markdown (Informal)

[Hierarchy-aware Biased Bound Margin Loss Function for Hierarchical Text Classification](https://aclanthology.org/2024.findings-acl.457) (Kim et al., Findings 2024)

• Hierarchy-aware Biased Bound Margin Loss Function for Hierarchical Text Classification (Kim et al., Findings 2024)

ACL

• Gibaeg Kim, SangHun Im, and Heung-Seon Oh. 2024. Hierarchy-aware Biased Bound Margin Loss Function for Hierarchical Text Classification. In Findings of the Association for Computational Linguistics ACL 2024, pages 7672–7682, Bangkok, Thailand and virtual meeting. Association for Computational Linguistics.