Academic

Not All Pretraining are Created Equal: Threshold Tuning and Class Weighting for Imbalanced Polarization Tasks in Low-Resource Settings

arXiv:2603.23534v1 Announce Type: new Abstract: This paper describes my submission to the Polarization Shared Task at SemEval-2025, which addresses polarization detection and classification in social media text. I develop Transformer-based systems for English and Swahili across three subtasks: binary polarization detection, multi-label target type classification, and multi-label manifestation identification. The approach leverages multilingual and African language-specialized models (mDeBERTa-v3-base, SwahBERT, AfriBERTa-large), class-weighted loss functions, iterative stratified data splitting, and per-label threshold tuning to handle severe class imbalance. The best configuration, mDeBERTa-v3-base, achieves 0.8032 macro-F1 on validation for binary detection, with competitive performance on multi-label tasks (up to 0.556 macro-F1). Error analysis reveals persistent challenges with implicit polarization, code-switching, and distinguishing heated political discourse from genuine polari

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Abass Oguntade · · 1 min read · 17 views
#cs.CL #cs.LG

arXiv:2603.23534v1 Announce Type: new Abstract: This paper describes my submission to the Polarization Shared Task at SemEval-2025, which addresses polarization detection and classification in social media text. I develop Transformer-based systems for English and Swahili across three subtasks: binary polarization detection, multi-label target type classification, and multi-label manifestation identification. The approach leverages multilingual and African language-specialized models (mDeBERTa-v3-base, SwahBERT, AfriBERTa-large), class-weighted loss functions, iterative stratified data splitting, and per-label threshold tuning to handle severe class imbalance. The best configuration, mDeBERTa-v3-base, achieves 0.8032 macro-F1 on validation for binary detection, with competitive performance on multi-label tasks (up to 0.556 macro-F1). Error analysis reveals persistent challenges with implicit polarization, code-switching, and distinguishing heated political discourse from genuine polarization.

Executive Summary

This article presents a novel approach to addressing polarization detection and classification in low-resource settings using multilingual and African language-specialized models. The authors leverage class-weighted loss functions, iterative stratified data splitting, and per-label threshold tuning to handle severe class imbalance. The best configuration achieves competitive performance on binary and multi-label tasks, but error analysis reveals persistent challenges with implicit polarization, code-switching, and distinguishing heated political discourse from genuine polarization. The study contributes to the development of effective methods for polarized text analysis, but its limitations highlight the need for continued research in this area.

Key Points

  • ▸ Develops Transformer-based systems for English and Swahili across three subtasks: binary polarization detection, multi-label target type classification, and multi-label manifestation identification.
  • ▸ Uses multilingual and African language-specialized models (mDeBERTa-v3-base, SwahBERT, AfriBERTa-large) to address low-resource settings.
  • ▸ Employes class-weighted loss functions, iterative stratified data splitting, and per-label threshold tuning to handle class imbalance.

Merits

Strength in Handling Class Imbalance

The study effectively addresses class imbalance in polarization detection tasks through the use of class-weighted loss functions and per-label threshold tuning.

Multilingual Model Utilization

The adoption of multilingual and African language-specialized models demonstrates the potential for leveraging diverse language expertise to improve polarized text analysis.

Cross-Lingual Transfer Learning

The study's focus on cross-lingual transfer learning facilitates the development of more versatile and adaptable models for low-resource languages.

Demerits

Limited Generalizability

The study's performance may be limited to specific language and task combinations, and its results may not generalize well to other languages or domains.

Lack of Interpretability

The study does not provide clear insights into the decision-making process of the models, making it challenging to understand the reasons behind their performance.

Inadequate Handling of Implicit Polarization

The study highlights the persistent challenges with implicit polarization, code-switching, and distinguishing heated political discourse from genuine polarization, suggesting a need for further research in these areas.

Expert Commentary

The study presents a novel approach to addressing polarization detection and classification in low-resource settings using multilingual and African language-specialized models. While the results are promising, the study's limitations highlight the need for continued research in this area. Specifically, the challenges with implicit polarization, code-switching, and distinguishing heated political discourse from genuine polarization suggest a need for further investigation. The study's contribution to the development of effective methods for polarized text analysis is significant, and its findings have practical and policy implications for the broader field of natural language processing.

Recommendations

  • ✓ Future research should focus on developing more interpretable models that can provide clear insights into their decision-making processes.
  • ✓ The use of pre-trained models and fine-tuning should be explored in greater depth to better understand their potential and limitations in low-resource settings.

Sources

Original: arXiv - cs.CL

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