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        <title>Aaron at SemEval-2026 Task 9: Multilingual Polarization Detection using Transformer-Based Models with Class Weighting and Threshold Tuning</title>
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        <name type="personal">
            <namePart type="given">Ekaterina</namePart>
            <namePart type="family">Kochmar</namePart>
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        <name type="personal">
            <namePart type="given">Debanjan</namePart>
            <namePart type="family">Ghosh</namePart>
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            <namePart type="given">Kai</namePart>
            <namePart type="family">North</namePart>
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            <namePart type="given">Mamoru</namePart>
            <namePart type="family">Komachi</namePart>
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    <abstract>This paper describes our submission to SemEval-2026 Task 9 on detecting multilingual, multicultural, and multievent online polarization. We address all three subtasks: binary polarization detection, polarization type classification, and manifestation identification for English and Swahili. Our approach leverages transformer-based models (RoBERTa-base for English, AfroXLMR-base for Swahili) with class-weighted loss functions to address severe label imbalance and per-label threshold tuning to optimize multi-label classification. On the test set, we achieve F1 macro scores of 0.7901 (English) and 0.7910 (Swahili) for Subtask 1, 0.4615 (English) and 0.4808 (Swahili) for Subtask 2 and 0.4791 (English) and 0.5830 (Swahili) for Subtask 3, which give competitive performance on the leaderboard, demonstrating the effectiveness of our methods for handling imbalanced multi-label polarization detection. Our error analysis reveals that models struggle with dehumanization detection and lack of empathy.</abstract>
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%0 Conference Proceedings
%T Aaron at SemEval-2026 Task 9: Multilingual Polarization Detection using Transformer-Based Models with Class Weighting and Threshold Tuning
%A Anampiu, Aaron
%Y Kochmar, Ekaterina
%Y Ghosh, Debanjan
%Y North, Kai
%Y Komachi, Mamoru
%S Proceedings of the 20th International Workshop on Semantic Evaluation (2026)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, USA
%@ 979-8-89176-414-9
%F anampiu-2026-aaron
%X This paper describes our submission to SemEval-2026 Task 9 on detecting multilingual, multicultural, and multievent online polarization. We address all three subtasks: binary polarization detection, polarization type classification, and manifestation identification for English and Swahili. Our approach leverages transformer-based models (RoBERTa-base for English, AfroXLMR-base for Swahili) with class-weighted loss functions to address severe label imbalance and per-label threshold tuning to optimize multi-label classification. On the test set, we achieve F1 macro scores of 0.7901 (English) and 0.7910 (Swahili) for Subtask 1, 0.4615 (English) and 0.4808 (Swahili) for Subtask 2 and 0.4791 (English) and 0.5830 (Swahili) for Subtask 3, which give competitive performance on the leaderboard, demonstrating the effectiveness of our methods for handling imbalanced multi-label polarization detection. Our error analysis reveals that models struggle with dehumanization detection and lack of empathy.
%U https://aclanthology.org/2026.semeval-1.286/
%P 2262-2267

Markdown (Informal)

[Aaron at SemEval-2026 Task 9: Multilingual Polarization Detection using Transformer-Based Models with Class Weighting and Threshold Tuning](https://aclanthology.org/2026.semeval-1.286/) (Anampiu, SemEval 2026)

• Aaron at SemEval-2026 Task 9: Multilingual Polarization Detection using Transformer-Based Models with Class Weighting and Threshold Tuning (Anampiu, SemEval 2026)

ACL

• Aaron Anampiu. 2026. Aaron at SemEval-2026 Task 9: Multilingual Polarization Detection using Transformer-Based Models with Class Weighting and Threshold Tuning. In Proceedings of the 20th International Workshop on Semantic Evaluation (2026), pages 2262–2267, San Diego, California, USA. Association for Computational Linguistics.