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

TL;DR

FiADD is a novel framework that improves implicit hate speech detection by aligning surface and implied forms and increasing label separation, demonstrating significant performance gains across multiple NLP tasks.

Contribution

Introduces FiADD, a new framework combining surface-meaning alignment and density discrimination to enhance implicit hate speech detection and generalize to related tasks.

Findings

Significant improvement in hate classification accuracy.

Effective generalization to sarcasm, irony, and stance detection.

Latent space analysis confirms the method's effectiveness.

Abstract

Although pretrained large language models (PLMs) have achieved state-of-the-art on many natural language processing (NLP) tasks, they lack an understanding of subtle expressions of implicit hate speech. Various attempts have been made to enhance the detection of implicit hate by augmenting external context or enforcing label separation via distance-based metrics. Combining these two approaches, we introduce FiADD, a novel Focused Inferential Adaptive Density Discrimination framework. FiADD enhances the PLM finetuning pipeline by bringing the surface form/meaning of an implicit hate speech closer to its implied form while increasing the inter-cluster distance among various 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, detecting sarcasm, irony, and stance, in which surface and implied forms differ, and observe similar performance improvements. Consequently, we analyze the generated latent space to understand its evolution under FiADD, which corroborates the advantage of employing FiADD for implicit hate speech detection.