@inproceedings{sun-etal-2025-zuifeng,
    title = "Zuifeng at {S}em{E}val-2025 Task 9: Multitask Learning with Fine-Tuned {R}o{BERT}a for Food Hazard Detection",
    author = "Sun, Dapeng  and
      Li, Sensen  and
      Wang, Yike  and
      Zhang, Shaowu",
    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-1.73/",
    pages = "527--531",
    ISBN = "979-8-89176-273-2",
    abstract = "This paper describes our system used in theSemEval-2025 Task 9 The Food Hazard Detec-tion Challenge. Through data processing thatremoves elements and shared multi-task archi-tecture improve the performance of detection.Without complex architectural modificationsthe proposed method achieves competitive per-formance with 0.7835 Marco F1-score on sub-task 1 and 0.4712 Marco F1-score on sub-task2. Comparative experiments reveal that jointprediction outperforms separate task trainingby 1.3{\%} F1-score, showing the effectiveness ofmulti-task learning of this challenge"
}