@inproceedings{s-etal-2025-pixel,
    title = "Pixel Phantoms at {S}em{E}val-2025 Task 11: Enhancing Multilingual Emotion Detection with a T5 and m{T}5-Based Approach",
    author = "S, Jithu Morrison  and
      Hariharakrishnan, Janani  and
      Singh, Harsh Pratap",
    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-1.86/",
    pages = "617--622",
    ISBN = "979-8-89176-273-2",
    abstract = "Emotion recognition in textual data is a crucial NLP task with applications in sentiment analysis and mental health monitoring. SemEval 2025 Task 11 introduces a multilingual dataset spanning 28 languages, including low-resource ones, to improve cross-lingual emotion detection. Our approach utilizes T5 for English and mT5 for other languages, fine-tuning them for multi-label classification and emotion intensity estimation. Our findings demonstrate the effectiveness of transformer-based models in capturing nuanced emotional expressions across diverse languages."
}