@inproceedings{verma-adelani-2025-mcgill,
    title = "{M}c{G}ill-{NLP} at {S}em{E}val-2025 Task 11: Bridging the Gap in Text-Based Emotion Detection",
    author = "Verma, Vivek  and
      Adelani, David Ifeoluwa",
    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-1.235/",
    pages = "1783--1789",
    ISBN = "979-8-89176-273-2",
    abstract = "In this paper, we present the results of our SemEval-2025 Emotion Detection Shared Task Track A which focuses on multi-label emotion detection. Our team{'}s approach leverages prompting GPT-4o, fine-tuning NLLB- LLM2Vec encoder, and an ensemble of these two approaches to solve Track A. Our ensemble method beats the baseline method that fine-tuned RemBERT encoder in 24 of the 28 languages. Furthermore, our results shows that the average performance is much worse for under-resourced languages in the Afro- Asiatic, Niger-Congo and Austronesia with per- formance scores at 50 F1 points and below."
}