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        <title>Team JAT at SemEval-2026 Task 9: Enhancing Polarization Detection with Cross-Lingual Transfer and Feature Fusion</title>
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        <namePart type="given">Aleksandra</namePart>
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            <title>Proceedings of the 20th International Workshop on Semantic Evaluation (2026)</title>
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            <namePart type="given">Ekaterina</namePart>
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    <abstract>We describe our system for SemEval-2026 Task9 (POLAR), Subtask 1 - binary polarizationdetection. Our approach investigates polariza-tion detection through monolingual and cross-lingual experimental settings. We first utilizea RoBERTa-based architecture enhanced withfeature fusion, combining contextual sentencerepresentations with handcrafted sentiment andintensity cues. As for multilingual joint train-ing, we explore it within the Indo-Europeanfamily to test whether cross-lingual transfer canelevate performance in data-scarce scenarios.Our final fine-tuned model achieves averageF1-score of 0.763 on the test set, compared to0.491 for a random baseline. We also reportablations for augmentation, feature fusion, andclass weighting to quantify each component’scontribution.</abstract>
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%0 Conference Proceedings
%T Team JAT at SemEval-2026 Task 9: Enhancing Polarization Detection with Cross-Lingual Transfer and Feature Fusion
%A Matkowska, Aleksandra
%A Lin, Taya
%A Chao, Yu-Chun
%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 matkowska-etal-2026-team
%X We describe our system for SemEval-2026 Task9 (POLAR), Subtask 1 - binary polarizationdetection. Our approach investigates polariza-tion detection through monolingual and cross-lingual experimental settings. We first utilizea RoBERTa-based architecture enhanced withfeature fusion, combining contextual sentencerepresentations with handcrafted sentiment andintensity cues. As for multilingual joint train-ing, we explore it within the Indo-Europeanfamily to test whether cross-lingual transfer canelevate performance in data-scarce scenarios.Our final fine-tuned model achieves averageF1-score of 0.763 on the test set, compared to0.491 for a random baseline. We also reportablations for augmentation, feature fusion, andclass weighting to quantify each component’scontribution.
%U https://aclanthology.org/2026.semeval-1.302/
%P 2402-2408

Markdown (Informal)

[Team JAT at SemEval-2026 Task 9: Enhancing Polarization Detection with Cross-Lingual Transfer and Feature Fusion](https://aclanthology.org/2026.semeval-1.302/) (Matkowska et al., SemEval 2026)

• Team JAT at SemEval-2026 Task 9: Enhancing Polarization Detection with Cross-Lingual Transfer and Feature Fusion (Matkowska et al., SemEval 2026)

ACL

• Aleksandra Matkowska, Taya Lin, and Yu-Chun Chao. 2026. Team JAT at SemEval-2026 Task 9: Enhancing Polarization Detection with Cross-Lingual Transfer and Feature Fusion. In Proceedings of the 20th International Workshop on Semantic Evaluation (2026), pages 2402–2408, San Diego, California, USA. Association for Computational Linguistics.