@inproceedings{ranjbar-baghbani-2025-lotus,
    title = "Lotus at {S}em{E}val-2025 Task 11: {R}o{BERT}a with Llama-3 Generated Explanations for Multi-Label Emotion Classification",
    author = "Ranjbar, Niloofar  and
      Baghbani, Hamed",
    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-1.60/",
    pages = "431--439",
    ISBN = "979-8-89176-273-2",
    abstract = "This paper presents a novel approach for multi-label emotion detection, where Llama-3 is used to generate explanatory content that clarifies ambiguous emotional expressions, thereby enhancing RoBERTa{'}s emotion classification performance. By incorporating explanatory context, our method improves F1-scores, particularly for emotions like fear, joy, and sadness, and outperforms text-only models. The addition of explanatory content helps resolve ambiguity, addresses challenges like overlapping emotional cues, and enhances multi-label classification, marking a significant advancement in emotion detection tasks."
}