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        <title>YNU-HPCC at SemEval-2025 Task 11: Bridging the Gap in Text-Based Emotion Using Multiple Prediction Headers</title>
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    <abstract>This paper describes the our team’s participation in Subtask A of Task 11 at SemEval-2025, focusing on multilingual text-based emotion classification. The team employed the RoBERTa model, enhanced with modifications to the output head to allow independent prediction of six emotions: anger, disgust, fear, joy, sadness, and surprise. The dataset was translated into English using Google Translate to facilitate processing. The study found that a single prediction head outperformed simultaneous prediction of multiple emotions, and training on the translated dataset yielded better results than using the original dataset. The team incorporated Focal Loss and R-Drop techniques to address class imbalance and improve model stability. Future work will continue to explore improvements in this area.</abstract>
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%0 Conference Proceedings
%T YNU-HPCC at SemEval-2025 Task 11: Bridging the Gap in Text-Based Emotion Using Multiple Prediction Headers
%A Yang, Hao
%A Wang, Jin
%A Zhang, Xuejie
%Y Rosenthal, Sara
%Y Rosá, Aiala
%Y Ghosh, Debanjan
%Y Zampieri, Marcos
%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 yang-etal-2025-ynu
%X This paper describes the our team’s participation in Subtask A of Task 11 at SemEval-2025, focusing on multilingual text-based emotion classification. The team employed the RoBERTa model, enhanced with modifications to the output head to allow independent prediction of six emotions: anger, disgust, fear, joy, sadness, and surprise. The dataset was translated into English using Google Translate to facilitate processing. The study found that a single prediction head outperformed simultaneous prediction of multiple emotions, and training on the translated dataset yielded better results than using the original dataset. The team incorporated Focal Loss and R-Drop techniques to address class imbalance and improve model stability. Future work will continue to explore improvements in this area.
%U https://aclanthology.org/2025.semeval-1.13/
%P 83-89

Markdown (Informal)

[YNU-HPCC at SemEval-2025 Task 11: Bridging the Gap in Text-Based Emotion Using Multiple Prediction Headers](https://aclanthology.org/2025.semeval-1.13/) (Yang et al., SemEval 2025)

• YNU-HPCC at SemEval-2025 Task 11: Bridging the Gap in Text-Based Emotion Using Multiple Prediction Headers (Yang et al., SemEval 2025)

ACL

• Hao Yang, Jin Wang, and Xuejie Zhang. 2025. YNU-HPCC at SemEval-2025 Task 11: Bridging the Gap in Text-Based Emotion Using Multiple Prediction Headers. In Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025), pages 83–89, Vienna, Austria. Association for Computational Linguistics.