Yu - Hsin Wu

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2025

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NYCU-NLP at SemEval-2025 Task 11: Assembling Small Language Models for Multilabel Emotion Detection and Intensity Prediction
Zhe - Yu Xu | Yu - Hsin Wu | Lung - Hao Lee
Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025)

This study describes the design of the NYCU-NLP system for the SemEval-2025 Task 11 that focuses on multi-lingual text-based emotion analysis. We instruction-tuned three small language models: Gemma-2 (27B), Mistral-small-3 (22B), and Phi-4 (14B) and then assembled them as our main system architecture. Our NYCU-NLP system participated the English Track A for multilabel emotion detection and English Track B for emotion intensity prediction. Experimental results show our best-performing submission produced a macro-averaging F1 score of 0.8225, ranking second of 90 participating teams for Track A, and ranked second among 41 teams for Track B with a Pearson correlation coefficient of 0.8373.
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