Papers › Hierarchy-aware Biased Bound Margin Loss Function for Hierarchical Text Classification
Hierarchy-aware Biased Bound Margin Loss Function for Hierarchical Text Classification
Gibaeg Kim, SangHun Im, Heung-Seon Oh
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.
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Introduced by this paper: HBM Loss
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