REGLAT at SemEval-2026 Task 9: Enhancing Arabic Online Polarization Detection Using AraBERT and Synonym Replacement Augmentation

2026-06-25 · Source: Paper Index on ACL Anthology · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Natural Language Processing · Depth: Expert, short

Summary

The REGLAT system, submitted to SemEval-2026 Task 9 (Subtask 1: Polarization Detection), addresses the binary classification of polarized content within Arabic social media text. This system employs a single-model approach, integrating a fine-tuned AraBERT model with synonym-based data augmentation to handle Arabic linguistic variations effectively. On the Arabic bind set, REGLAT achieved a competitive macro F1-score of 0.831 and an accuracy of 0.833. Among 45 participating teams, the system secured the 11th rank overall, demonstrating a performance gap of only 0.018 macro F1 from the top-ranked team, which scored 0.8488. The results highlight that this combination of fine-tuned AraBERT and synonym replacement offers a robust, simple, and reproducible baseline, often surpassing more intricate configurations in detecting Arabic attitude polarization nuances.

Key takeaway

For NLP Engineers developing Arabic social media analysis tools, you should consider fine-tuned AraBERT with synonym replacement as a primary baseline for polarization detection. This approach offers a simple, reproducible, and highly competitive solution, achieving a 0.831 macro F1-score. It demonstrates that effective data augmentation can significantly enhance model performance for nuanced Arabic linguistic variations, potentially saving development time compared to more complex architectures.

Key insights

Fine-tuned AraBERT with synonym replacement data augmentation provides a strong, simple baseline for Arabic online polarization detection.

Principles

• Simple, reproducible baselines can outperform complex models.
• Data augmentation via synonym replacement enhances performance for linguistic variations.

Method

A single-model approach combines fine-tuned AraBERT with synonym-based data augmentation for binary classification of polarized Arabic social media text.

In practice

• Apply synonym replacement for data augmentation in Arabic NLP tasks.
• Utilize fine-tuned AraBERT for social media polarization detection.

Topics

• Arabic NLP
• Polarization Detection
• AraBERT
• Data Augmentation
• Social Media Analysis
• SemEval-2026

Best for: AI Scientist, NLP Engineer, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Paper Index on ACL Anthology.