@inproceedings{aryal-pant-2025-howard,
    title = "{H}oward {U}niversity-{AI}4{PC} at {S}em{E}val-2025 Task 9: Using Open-weight {BART}-{MNLI} for Zero Shot Classification of Food Recall Documents",
    author = "Aryal, Saurav  and
      Pant, Kritika",
    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-1.250/",
    pages = "1919--1923",
    ISBN = "979-8-89176-273-2",
    abstract = "We present our system for SemEval-2025 Task 9: Food Hazard Detection, a shared task focused on the explainable classification of food-incident reports. The task involves predicting hazard and product categories (ST1) and their exact vectors (ST2) from short texts. Our approach leverages zero-shot classification using the BART-large-MNLI model, which allows classification without task-specific fine-tuning. Our model achieves competitive performance, emphasizing hazard prediction accuracy, as evaluated by the macro-F1 score."
}