Hierarchy-aware Biased Bound Margin Loss Function for Hierarchical Text Classification
Gibaeg Kim, SangHun Im, Heung‐Seon Oh · 2024
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. 1