@inproceedings{khan-etal-2025-nust-titans,
    title = "{NUST} Titans at {S}em{E}val-2025 Task 11: {A}fro{E}mo: Multilingual Emotion Detection with Adaptive {A}fro-{XLM}-{R}",
    author = "Khan, Maham  and
      Fatima, Mehwish  and
      Naeem, Hajra  and
      Rana, Laiba  and
      Khan, Faiza  and
      Latif, Seemab  and
      Shahzad, Khurram",
    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-1.289/",
    pages = "2225--2232",
    ISBN = "979-8-89176-273-2",
    abstract = "This paper presents AfroEmo, a multilingual, multi label emotion classification system designed for SemEval 2025 Task 11, leveraging the Afro XLMR model. Our approach integrates adaptive pretraining on domain specific corpora followed by fine tuning on low resource languages. Through comprehensive exploratory data analysis, we assess label distribution and model performance across diverse linguistic settings. By incorporating perceived emotions, how emotions are interpreted rather than explicitly stated, we enhance emotion recognition capabilities in underrepresented languages. Experimental results demonstrate that our method achieves competitive performance particularly in Amharic, while addressing key challenges in low resource emotion detection."
}