EDAL: Entropy based Dynamic Attention Loss for HateSpeech Classification

Entropy-based Dynamic Attention Loss" (EDAL) to enhance model interpretability by incorporating an additional attention layer. EDAL encourages attention scores that provide valuable insights and boosts the performance of pretrained models during fine-tuning for downstream tasks. We conduct extensive experiments on six diverse datasets, confirming that EDAL effectively enhances classification performance while maintaining interpretability. Additionally, experiments with various pretrained models demonstrate EDAL's significant performance improvements during fine-tuning. In summary, EDAL holds promise for creating more transparent and reliable hate speech classifiers, contributing to a safer online environment.