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2025 ACL ACL 2025

FiRC-NLP at SemEval-2025 Task 11: To Prompt or to Fine-Tune? Approaches for Multilingual Emotion Classification

Abstract

AbstractIn this paper, we describe our system devel-oped for participation in SemEval-2025 Task11: Bridging the Gap in Text-Based EmotionDetection. We compare three approaches formultilingual, multi-label emotion classification:XLM-R, an ensemble of models (XLM-5), anda prompt-based approach. We evaluate the per-formance of these models across a diverse setof languages, ranging from high-resource tolow-resource languages

❓ The Questioner
🌉 Interdisciplinary Bridge — Artificial Intelligence and Deep Learning and Machine Learning
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio

Authors

Wondimagegnhue Tufa , Fadi Hassan , Evgenii Migaev , Yalei Fu

Topics

Artificial Intelligence > Learning Paradigms > Transfer Learning Machine Learning > Core Methods > Classification Machine Learning > Learning Types > Prompt Engineering Deep Learning > Learning Types > Fine-Tuning

Keywords

ensemble learning prompt engineering prompt-based learning emotion classification multilingual model multilingual classification
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