Aligning Implied Statements for Implicit Hate Speech Generalizability with Context-Bounded Semi-hard Negative Mining

2026-06-18 · Source: cs.CL updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Natural Language Processing · Depth: Expert, medium

Summary

The ImpSH framework, proposed by Wicaksono Leksono Muhamad and Yunita Sari, addresses the challenge of classifying implicit hate speech, where intent is often masked. This triplet-based approach aligns posts with implied statements and employs context-bounded semi-hard negatives to focus learning on near confusions. It tackles issues like overfitting surface cues and poor cross-dataset transfer seen in prior supervised contrastive methods. A variant, AugSH, uses data augmentation for positives. Evaluated on IHC, SBIC, and DynaHate datasets with BERT and HateBERT, ImpSH demonstrates viability against standard baselines, often improving cross-domain performance. Representation analysis confirms tighter positive pairs and balanced global spread, offering a stable mapping for insinuations.

Key takeaway

For machine learning engineers developing implicit hate speech classifiers, if you struggle with cross-domain generalization due to semantic overlap, consider implementing a triplet-based framework like ImpSH. By aligning posts with implied statements and using context-bounded semi-hard negative mining, you can achieve more stable representations and improve transfer performance across datasets. This approach helps overcome the volatility of traditional clustering-based methods.

Key insights

Aligning implied statements with context-bounded semi-hard negative mining improves implicit hate speech detection generalizability.

Principles

• Implicit hate detection needs intent inference.
• Semantic overlap blurs decision boundaries.
• Negative selection is critical for SCL.

Method

The ImpSH framework jointly optimizes a standard cross-entropy loss and a triplet loss. It mines context-bounded semi-hard negatives within each minibatch, selecting opposite-label negatives farther than the positive but within a margin.

In practice

• Use implied statements for positive pairs.
• Apply data augmentation for positives.
• Evaluate with alignment and uniformity scores.

Topics

• Implicit Hate Speech
• Contrastive Learning
• Triplet Loss
• Negative Mining
• Cross-Domain Generalization
• HateBERT

Code references

• airlanggawicaksono/acl-future

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, NLP Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CL updates on arXiv.org.