@inproceedings{afshari-etal-2025-mrs,
    title = "{MRS} at {S}em{E}val-2025 Task 11: A Hybrid Approach for Bridging the Gap in Text-Based Emotion Detection",
    author = "Afshari, Milad  and
      Frost, Richard  and
      Kissel, Samantha  and
      Johnson, Kristen",
    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-1.81/",
    pages = "584--589",
    ISBN = "979-8-89176-273-2",
    abstract = "We tackle the challenge of multi-label emotion detection in short texts, focusing on SemEval-2025 Task 11 Track A. Our approach, RoEmo, combines generative and discriminative models in an ensemble strategy to classify texts into five emotions: anger, fear, joy, sadness, and surprise.The generative model, instruction-finetuned on emotion detection datasets, undergoes additional fine-tuning on the SemEval-2025 Task 11 Track A dataset to enhance its performance for this specific task. Meanwhile, the discriminative model, based on binary classification, offers a straightforward yet effective approach to classification.We review recent advancements in multi-label emotion detection and analyze the task dataset. Our results show that RoEmo ranks among the top-performing systems, demonstrating high accuracy and reliability."
}