@inproceedings{yip-etal-2025-charsiurice,
    title = "{C}harsiu{R}ice at {S}em{E}val-2025 Task 11: Bridging the Gap in Text-Based Emotion Detection",
    author = "Yip, Hiu Yan  and
      Chiu, Hing Man  and
      Yang, Hai - Yin",
    editor = "Rosenthal, Sara  and
      Ros{\'a}, Aiala  and
      Ghosh, Debanjan  and
      Zampieri, Marcos",
    booktitle = "Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/corrections-2025-08/2025.semeval-1.143/",
    pages = "1082--1088",
    ISBN = "979-8-89176-273-2",
    abstract = "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."
}