transformer_1376 at SemEval-2026 Task 9: A Multi-Stage Pipeline with Calibrated Ensembles and Lexical Post-Processing for Online Polarization Detection in Bengali

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

Summary

The "transformer_1376" team presented a robust classification system for Subtask 1 of the POLAR @ SemEval-2026 Task 9, specifically targeting the detection of online polarization in Bengali textual sequences. This system addresses the binary classification of polarized stances within diverse multilingual and multicultural online environments. Their methodology centers on fine-tuning the BanglaBERT Large model, augmented by a sophisticated multi-stage pipeline. Key components include a stratified five-fold cross-validation approach, a performance-weighted ensemble method, temperature scaling for probability calibration, and a set of lexical post-processing rules. This comprehensive system aims to accurately identify polarized content, contributing significantly to the broader challenge of analyzing online discourse.

Key takeaway

For NLP Engineers developing online content moderation systems, this work demonstrates a robust approach to detecting polarized stances. You should consider integrating fine-tuned language-specific BERT models, like BanglaBERT Large, with multi-stage pipelines that include stratified cross-validation, performance-weighted ensembles, and probability calibration. This methodology can significantly improve the accuracy and reliability of your binary classification systems, especially for low-resource languages or culturally nuanced content, by refining model outputs through lexical post-processing.

Key insights

A multi-stage pipeline combining fine-tuned BERT, calibrated ensembles, and lexical rules effectively detects online polarization in Bengali.

Principles

• Fine-tune large language models for domain-specific tasks.
• Integrate ensemble methods with probability calibration for robustness.
• Lexical rules can enhance classification post-processing.

Method

Fine-tuning BanglaBERT Large, applying stratified five-fold cross-validation, a performance-weighted ensemble, temperature scaling probability calibration, and lexical post-processing rules.

In practice

• Fine-tune language-specific BERT models for local NLP.
• Employ calibrated ensembles for reliable text classification.
• Apply lexical rules to refine model predictions.

Topics

• Online Polarization Detection
• Bengali NLP
• BanglaBERT Large
• Ensemble Learning
• Probability Calibration
• Text Classification

Best for: Research Scientist, AI Scientist, NLP Engineer

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