@inproceedings{zhu-etal-2025-tinaal,
    title = "tinaal at {S}em{E}val-2025 Task 11: Enhancing Perceived Emotion Intensity Prediction with Boosting Fine-Tuned Transformers",
    author = "Zhu, Ting  and
      Huang, Liting  and
      Liang, Huizhi(elly)",
    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/transition-to-people-yaml/2025.semeval-1.43/",
    pages = "302--307",
    ISBN = "979-8-89176-273-2",
    abstract = "This paper presents a framework for perceived emotion intensity prediction, focusing on SemEval-2025 Task 11 Track B. The task involves predicting the intensity of five perceived emotions{---}anger, fear, joy, sadness, and surprise{---}on an ordinal scale from 0 (no emotion) to 3 (high emotion). Our approach builds upon our method introduced in the WASSA workshop and enhances it by integrating ModernBERT in place of the traditional BERT model within a boosting-based ensemble framework. To address the difficulty in capturing fine-grained emotional distinctions, we incorporate class-preserving mixup data augmentation, a custom Pearson CombinLoss function, and fine-tuned transformer models, including ModernBERT, RoBERTa, and DeBERTa. Compared to individual fine-tuned transformer models (BERT, RoBERTa, DeBERTa and ModernBERT) without augmentation or ensemble learning, our approach demonstrates significant improvements. The proposed system achieves an average Pearson correlation coefficient of 0.768 on the test set, outperforming the best individual baseline model. In particular, the model performs best for sadness (r = 0.808) and surprise (r = 0.770), highlighting its ability to capture subtle intensity variations in the text. Despite these improvements, challenges such as data imbalance, performance on low-resource emotions (e.g., anger and fear), and the need for refined data augmentation techniques remain open for future research."
}