@inproceedings{vaidyanathan-etal-2025-nlp-goats,
    title = "{NLP}{\_}goats at {S}em{E}val-2025 Task 11: Multi-Label Emotion Classification Using Fine-Tuned Roberta-Large Tranformer",
    author = "Vaidyanathan, Vijay Karthick  and
      V K, Srihari  and
      D U, Mugilkrishna  and
      Madhavan, Saritha",
    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-1.135/",
    pages = "1023--1027",
    ISBN = "979-8-89176-273-2",
    abstract = "This paper serves as a solution for multi-label emotion classification and intensity for text, developed for SemEval-2025 Task 11. The method uses a fine-tuned RoBERTa-Large transformer model. The system represents a multi-label classification approach to identifying multiple emotions, and uses regression models to estimate emotion strength. The model performed with ranks of 31st and 17th place in the corresponding tracks. The findings show impressive performance and it remains possible to improve the performance of ambiguous or low-frequency emotion recognition using the state-of-the-art contextual embeddings and threshold optimization techniques."
}