@inproceedings{kesanam-etal-2025-nitk,
    title = "{NITK}-{VITAL} at {S}em{E}val-2025 Task 11: Focal-{R}o{BERT}a: Addressing Class Imbalance in Multi-Label Emotion Classification",
    author = "Kesanam, Ashinee  and
      Venkata Ravi Ram, Gummuluri  and
      Banoth, Chaithanya Swaroop  and
      Reddy, G Rama Mohana",
    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-1.142/",
    pages = "1077--1081",
    ISBN = "979-8-89176-273-2",
    abstract = "This paper presents our approach to SemEval Task 11, which focuses on multi-label emotion detection in English textual data. We experimented with multiple methodologies, including traditional machine learning models, deep learning architectures, and transformer-based models. Our best-performing approach employed RoBERTa with focal loss, which effectively mitigated class imbalances and achieved a macro F1-score of 0.7563, outperforming other techniques. Comparative analyses between different embedding strategies, such as TF-IDF, BERT, and MiniLM, revealed that transformer-based models consistently provided superior performance. The results demonstrate the effectiveness of focal loss in handling highly skewed emotion distributions. Our system contributes to advancing multi-label emotion detection by leveraging robust pre-trained models and loss function optimization."
}