@inproceedings{sharp-etal-2025-team,
    title = "Team {K}i{A}m{S}o at {S}em{E}val-2025 Task 11: A Comparison of Classification Models for Multi-label Emotion Detection",
    author = {Sharp, Kimberly  and
      Kathmann, Sofia  and
      R{\"u}eck, Amelie},
    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/ingest-emnlp/2025.semeval-1.203/",
    pages = "1542--1548",
    ISBN = "979-8-89176-273-2",
    abstract = "The aim of this paper is to take on the challenge of multi-label emotion detection for a variety of languages as part of Track A in SemEval 2025 Task 11: Bridging the Gap in Text-Based Emotion Detection. We fine-tune different pre-trained mono- and multilingual language models and compare their performance on multi-label emotion detection on a variety of high-resource and low-resource languages. Overall, we find that monolingual models tend to perform better, but for low-resource languages that do not have state-of-the-art pre-trained language models, multilingual models can achieve comparable results."
}