@inproceedings{paduraru-2025-team,
    title = "Team {UBD} at {S}em{E}val-2025 Task 11: Balancing Class and Task Importance for Emotion Detection",
    author = "Paduraru, Cristian",
    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-1.119/",
    pages = "874--884",
    ISBN = "979-8-89176-273-2",
    abstract = "This article presents the systems used by Team UBD in Task 11 of SemEval-2025. We participated in all three sub-tasks, namely Emotion Detection, Emotion Intensity Estimation and Cross-Lingual Emotion Detection. In our solutions we make use of publicly available Language Models (LMs) already fine-tuned for the Emotion Detection task, as well as open-sourced models for Neural Machine Translation (NMT). We robustly adapt the existing LMs to the new data distribution, balance the importance of all emotions and classes and also use a custom sampling scheme.We present fine-grained results in all sub-tasks and analyze multiple possible sources for errors for the Cross-Lingual Emotion Detection sub-task."
}