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Although pre-trained large language models (PLMs) have achievedstate-of-the-art on many NLP tasks, they lack understanding of subtleexpressions of implicit hate speech. Such nuanced and implicit hate is oftenmisclassified as non-hate. Various attempts have been made to enhance thedetection of (implicit) hate content by augmenting external context orenforcing label separation via distance-based metrics. We combine these twoapproaches and introduce FiADD, a novel Focused Inferential Adaptive DensityDiscrimination framework. FiADD enhances the PLM finetuning pipeline bybringing the surface form of an implicit hate speech closer to its implied formwhile increasing the inter-cluster distance among various class labels. We testFiADD on three implicit hate datasets and observe significant improvement inthe two-way and three-way hate classification tasks. We further experiment onthe generalizability of FiADD on three other tasks, namely detecting sarcasm,irony, and stance, in which surface and implied forms differ, and observesimilar performance improvement. We analyze the generated latent space tounderstand its evolution under FiADD, which corroborates the advantage ofemploying FiADD for implicit hate speech detection.

Focal Inferential Infusion Coupled with Tractable Density Discrimination for Implicit Hate Speech Detection