@inproceedings{sinha-gamba-2025-fossils,
    title = "Fossils at {S}em{E}val-2025 Task 9: Tasting Loss Functions for Food Hazard Detection in Text Reports",
    author = "Sinha, Aman  and
      Gamba, Federica",
    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-1.199/",
    pages = "1515--1521",
    ISBN = "979-8-89176-273-2",
    abstract = "Food hazard detection is an emerging field where NLP solutions are being explored. Despite the recent accessibility of powerful language models, one of the key challenges that still persists is the high class imbalance within datasets, often referred to in the literature as the {\{}textit{\{}long tail problem{\}}{\}}.In this work, we present a study exploring different loss functions borrowed from the field of visual recognition, to tackle long-tailed class imbalance for food hazard detection in text reports. Our submission to SemEval-2025 Task 9 on the Food Hazard Detection Challenge shows how re-weighting mechanism in loss functions prove beneficial in class imbalance scenarios. In particular, we empirically show that class-balanced and focal loss functions outperform all other loss strategies for Subtask 1 and 2 respectively."
}