Frozen BGE embeddings with per-emotion prompts and a CatBoost classifier outperform fully fine-tuned mBERT/XLM-R and surpass prior decoder baselines on 28-language emotion detection.
In Proceedings of the 18th International Workshop on Semantic Evalua- tion (SemEval-2024), pages 1405–1411, Mexico City, Mexico
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University of Indonesia at SemEval-2025 Task 11: Evaluating State-of-the-Art Encoders for Multi-Label Emotion Detection
Frozen BGE embeddings with per-emotion prompts and a CatBoost classifier outperform fully fine-tuned mBERT/XLM-R and surpass prior decoder baselines on 28-language emotion detection.