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        <title>DeepSemantics at SemEval-2026 Task 9: Label-Wise Optimization with Adaptive Focal Loss for Polarization Manifestation Identification</title>
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    <abstract>In this paper, we present our system for SemEval-2026 Task 9, which focuses on the fine-grained identification of polarization manifestations in multilingual social media content.Our approach combines transformer-based encoders (RoBERTa-base for English and Afro-XLM-R-small for Hausa) within aOne-vs-Rest (OvR) framework, complemented by controlled oversampling, Adaptive Focal Loss, and label-wise threshold optimization. To mitigate severe class imbalance and label sparsity, we adopt language-specific optimization strategies supported by pairwise \ensuremathχ2 independence analysis.Our system achieves macro-F1 scores of 0.464 in English and 0.192 in Hausa on the official test sets, ranking 5th in Hausa and 14th in English on the official leaderboard.</abstract>
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%0 Conference Proceedings
%T DeepSemantics at SemEval-2026 Task 9: Label-Wise Optimization with Adaptive Focal Loss for Polarization Manifestation Identification
%A Tiao, Eliasse
%A Edou, Josue
%A Gohouede, Mahugnon
%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 tiao-etal-2026-deepsemantics
%X In this paper, we present our system for SemEval-2026 Task 9, which focuses on the fine-grained identification of polarization manifestations in multilingual social media content.Our approach combines transformer-based encoders (RoBERTa-base for English and Afro-XLM-R-small for Hausa) within aOne-vs-Rest (OvR) framework, complemented by controlled oversampling, Adaptive Focal Loss, and label-wise threshold optimization. To mitigate severe class imbalance and label sparsity, we adopt language-specific optimization strategies supported by pairwise \ensuremathχ2 independence analysis.Our system achieves macro-F1 scores of 0.464 in English and 0.192 in Hausa on the official test sets, ranking 5th in Hausa and 14th in English on the official leaderboard.
%U https://aclanthology.org/2026.semeval-1.210/
%P 1632-1640

Markdown (Informal)

[DeepSemantics at SemEval-2026 Task 9: Label-Wise Optimization with Adaptive Focal Loss for Polarization Manifestation Identification](https://aclanthology.org/2026.semeval-1.210/) (Tiao et al., SemEval 2026)

• DeepSemantics at SemEval-2026 Task 9: Label-Wise Optimization with Adaptive Focal Loss for Polarization Manifestation Identification (Tiao et al., SemEval 2026)

ACL

• Eliasse Tiao, Josue Edou, and Mahugnon Gohouede. 2026. DeepSemantics at SemEval-2026 Task 9: Label-Wise Optimization with Adaptive Focal Loss for Polarization Manifestation Identification. In Proceedings of the 20th International Workshop on Semantic Evaluation (2026), pages 1632–1640, San Diego, California, USA. Association for Computational Linguistics.