University of Indonesia at SemEval-2025 Task 11: Evaluating State-of-the-Art Encoders for Multi-Label Emotion Detection

• URL: http://arxiv.org/abs/2505.16460v1
• Date: Thu, 22 May 2025 09:42:11 GMT
• Title: University of Indonesia at SemEval-2025 Task 11: Evaluating State-of-the-Art Encoders for Multi-Label Emotion Detection
• Authors: Ikhlasul Akmal Hanif, Eryawan Presma Yulianrifat, Jaycent Gunawan Ongris, Eduardus Tjitrahardja, Muhammad Falensi Azmi, Rahmat Bryan Naufal, Alfan Farizki Wicaksono,
• Abstract summary: This paper focuses on multilabel emotion classification across 28 languages.<n>We explore two main strategies: fully fine-tuning transformer models and classifier-only training.<n>Our findings suggest that training a classifier on top of prompt-based encoders such as mE5 and BGE yields significantly better results than fully fine-tuning XLMR and mBERT.
• Score: 1.2564343689544841
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper presents our approach for SemEval 2025 Task 11 Track A, focusing on multilabel emotion classification across 28 languages. We explore two main strategies: fully fine-tuning transformer models and classifier-only training, evaluating different settings such as fine-tuning strategies, model architectures, loss functions, encoders, and classifiers. Our findings suggest that training a classifier on top of prompt-based encoders such as mE5 and BGE yields significantly better results than fully fine-tuning XLMR and mBERT. Our best-performing model on the final leaderboard is an ensemble combining multiple BGE models, where CatBoost serves as the classifier, with different configurations. This ensemble achieves an average F1-macro score of 56.58 across all languages.

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