StanceLab at SemEval-2026 Task 9: Addressing Class Imbalance in Multilingual Polarization Detection

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

Summary

StanceLab participated in SemEval-2026 Task 9, focusing on multilingual polarization detection across 22 languages. Their staged experimental strategy first explored the problem in monolingual English before expanding to multilingual models. The system evaluated several transformer-based architectures, including RoBERTa, XLM-RoBERTa, MPNet, and mDeBERTa-v3. To address class imbalance, techniques such as weighted loss functions, focal loss, and data augmentation via back-translation and large language models were employed. Experimental results indicated that no single configuration consistently outperformed others across all languages. However, focal loss and data augmentation frequently enhanced performance in languages exhibiting skewed label distributions, underscoring the importance of contextual representations and imbalance-aware training.

Key takeaway

For NLP engineers developing multilingual text classification systems, particularly for polarization detection, you should prioritize integrating imbalance-aware training strategies like focal loss. Consider employing data augmentation techniques such as back-translation or large language models to improve performance in languages with skewed label distributions. Your approach should also account for language-specific nuances, as no single model configuration dominates universally.

Key insights

Multilingual polarization detection benefits from imbalance-aware training and language-specific considerations.

Principles

• Contextual representations are crucial for robust detection.
• Imbalance-aware training strategies improve performance.
• Language-specific considerations are vital for multilingual tasks.

Method

A staged experimental strategy investigates monolingual English first, then extends to multilingual modeling, combining transformer architectures with weighted loss, focal loss, and data augmentation.

In practice

• Apply focal loss for skewed label distributions.
• Use back-translation or LLMs for data augmentation.

Topics

• Multilingual Polarization Detection
• SemEval-2026
• Class Imbalance
• Transformer Models
• Focal Loss
• Data Augmentation

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

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