@inproceedings{chu-2025-cdhf,
    title = "{CDHF} at {S}em{E}val-2025 Task 9: A Multi-Task Learning Approach for Food Hazard Classification",
    author = "Chu, Phuoc",
    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/transition-to-people-yaml/2025.semeval-1.282/",
    pages = "2177--2182",
    ISBN = "979-8-89176-273-2",
    abstract = "We present our system in SemEval-2025 Task 9: Food Hazard Detection. Our approach focuses on multi-label classification of food recall titles into predefined hazard and product categories. We fine-tune pre-trained transformer models, comparing BERT and BART. Our results show that BART significantly outperforms BERT, achieving an F1-score of 0.8033 during development. However, in the final evaluation phase, our system obtained an F1-score of 0.7676, ranking 54th in Subtask 1. While our performance is not among the top, our findings highlight the importance of model choice in food hazard classification. Future work can explore additional improvements, such as ensemble methods and domain adaptation"
}