Gradient Descenders at SemEval-2026 Task 9: Data-Centric Counterfactual Augmentation for Multi-Label Hate Speech Detection
Year: 2026
Type: conference-paper
Abstract: In this paper, we describe the Gradient Descenders submission to SemEval-2026 Task 9 Subtask 2: Multi-Label Hate Speech Detection.Existing Transformer-based approaches often exhibit degraded performance on this task due to severe class imbalance and complex class intersectionality, leading to the learning of spurious correlations.To counteract this, we introduce a novel, data-centric counterfactual augmentation pipeline.We employ Large Language Models (LLMs) as semantic generators to synthesize diverse, targeted training samples via three distinct prompting strategies: Additive Label-Flipping (Attribute Injection), Context Decoupling, and Cross-Domain Identity Substitution.Fine-tuning a RoBERTa classifier on this augmented corpus significantly improves the model's sensitivity to minority classes.Ultimately, our system achieves a Macro-F1 score of 44.15% on the official test set, highlighting the efficacy of targeted LLM-based augmentation in highly imbalanced, multi-label environments.
Authors: Tran Nhan, Dang Thin
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
SDG: Reduced inequalities
Open Access status: hybrid
OpenAlex ID: W7166832033