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    <abstract>In this SemEval 2025 Task 11 paper, we tackled three tracks: Multi-label Emotion Detection, Emotion Intensity, and Cross-lingual Emotion Detection. Our approach harnesses diverse external corpora and robust data augmentation techniques across Spanish, English, and Arabic, enhancing both the diversity and resilience of the dataset. Instead of developing separate models for each language, we merge the data into a unified multilingual dataset, enabling our model to learn cross-lingual patterns and relationships simultaneously. Our ensemble architecture integrates the multilingual strengths of XLM-RoBERTa, a zero-shot classification capability via LLaMA 3, and a specialized pretrained model fine-tuned on English emotion classification. Notably, our system achieved strong performance, ranking 13th for Afrikaans (afr) in Track A, 13th for Amharic (amh) in Track B, and 4th for Hindi (hin) in Track C.</abstract>
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%0 Conference Proceedings
%T UB_Tel-U at SemEval-2025 Task 11: Emotions Without Borders - A Unified Framework for Multilingual Classification Using Augmentation and Ensemble
%A Fatyanosa, Tirana Noor
%A Adikara, Putra Pandu
%A Erfitra, Rochmanu Purnomohadi
%A Alkautsar Dikna, Muhammad Rajendra
%A Budiwati, Sari Dewi
%Y Rosenthal, Sara
%Y Rosá, Aiala
%Y Ghosh, Debanjan
%Y Zampieri, Marcos
%A Cahyana
%S Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-273-2
%F fatyanosa-etal-2025-ub
%X In this SemEval 2025 Task 11 paper, we tackled three tracks: Multi-label Emotion Detection, Emotion Intensity, and Cross-lingual Emotion Detection. Our approach harnesses diverse external corpora and robust data augmentation techniques across Spanish, English, and Arabic, enhancing both the diversity and resilience of the dataset. Instead of developing separate models for each language, we merge the data into a unified multilingual dataset, enabling our model to learn cross-lingual patterns and relationships simultaneously. Our ensemble architecture integrates the multilingual strengths of XLM-RoBERTa, a zero-shot classification capability via LLaMA 3, and a specialized pretrained model fine-tuned on English emotion classification. Notably, our system achieved strong performance, ranking 13th for Afrikaans (afr) in Track A, 13th for Amharic (amh) in Track B, and 4th for Hindi (hin) in Track C.
%U https://aclanthology.org/2025.semeval-1.253/
%P 1948-1956

Markdown (Informal)

[UB_Tel-U at SemEval-2025 Task 11: Emotions Without Borders - A Unified Framework for Multilingual Classification Using Augmentation and Ensemble](https://aclanthology.org/2025.semeval-1.253/) (Fatyanosa et al., SemEval 2025)

• UB_Tel-U at SemEval-2025 Task 11: Emotions Without Borders - A Unified Framework for Multilingual Classification Using Augmentation and Ensemble (Fatyanosa et al., SemEval 2025)

ACL

• Tirana Noor Fatyanosa, Putra Pandu Adikara, Rochmanu Purnomohadi Erfitra, Muhammad Rajendra Alkautsar Dikna, Sari Dewi Budiwati, and Cahyana. 2025. UB_Tel-U at SemEval-2025 Task 11: Emotions Without Borders - A Unified Framework for Multilingual Classification Using Augmentation and Ensemble. In Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025), pages 1948–1956, Vienna, Austria. Association for Computational Linguistics.