AmpleHate: Amplifying the Attention for Versatile Implicit Hate Detection

Implicit hate speech involves subtle and indirect expressions of prejudice or hostility toward a group.Detecting it is challenging because it relies on nuanced context and implication rather than explicit offensive language.Current approaches rely on contrastive learning, which is shown to be effective on distinguishing hate and non-hate sentences.Humans, however, detect implicit hate speech by first identifying specific targets within the text and subsequently interpreting how these targets relate to their surrounding context.Motivated by this reasoning process, we propose Ample-Hate, a novel approach designed to mirror human inference for implicit hate detection.Am-pleHate identifies explicit targets using a pretrained Named Entity Recognition model and captures implicit target information via [CLS] tokens.It computes attention-based relationships between explicit, implicit targets and sentence context and then, directly injects these relational vectors into the final sentence representation.This amplifies the critical signals of target-context relations for determining implicit hate.Experiments demonstrate that Am-pleHate achieves state-of-the-art performance, outperforming contrastive learning baselines by an average of 82.14% and achieves faster convergence.Qualitative analyses further reveal that attention patterns produced by Am-pleHate closely align with human judgement, underscoring its interpretability and robustness.