Hiu Yan Yip
2025
CharsiuRice at SemEval-2025 Task 11: Bridging the Gap in Text-Based Emotion Detection
Hiu Yan Yip
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Hing Man Chiu
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Hai - Yin Yang
Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025)
This paper presents our participation in SemEval-2025 Task 11, which focuses on bridging the gap in text-based emotion detection. Our team took part in both Tracks A and B, addressing different aspects of emotion classification. We fine-tuned a RoBERTa base model on the provided dataset in Track A, achieving a Macro-F1 score of 0.7264. For Track B, we built on top of the Track A model by incorporating an additional non-linear layer, in the hope of enhancing Track A model’s understanding of emotion detection. Track B model resulted with an average Pearson’s R of 0.5658. The results demonstrate the effectiveness of fine-tuning in Track A and the potential improvements from architectural modifications in Track B for emotion intensity detection tasks.