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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
%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.
%R 10.18653/v1/2024.findings-acl.457
%U https://aclanthology.org/2024.findings-acl.457
%U https://doi.org/10.18653/v1/2024.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. Association for Computational Linguistics.