  Although pre-trained large language models (PLMs) have achieved
state-of-the-art on many NLP tasks, they lack understanding of subtle
expressions of implicit hate speech. Such nuanced and implicit hate is often
misclassified as non-hate. Various attempts have been made to enhance the
detection of (implicit) hate content by augmenting external context or
enforcing label separation via distance-based metrics. We combine these two
approaches and introduce FiADD, a novel Focused Inferential Adaptive Density
Discrimination framework. FiADD enhances the PLM finetuning pipeline by
bringing the surface form of an implicit hate speech closer to its implied form
while increasing the inter-cluster distance among various class labels. We test
FiADD on three implicit hate datasets and observe significant improvement in
the two-way and three-way hate classification tasks. We further experiment on
the generalizability of FiADD on three other tasks, namely detecting sarcasm,
irony, and stance, in which surface and implied forms differ, and observe
similar performance improvement. We analyze the generated latent space to
understand its evolution under FiADD, which corroborates the advantage of
employing FiADD for implicit hate speech detection.
