@inproceedings{deng-etal-2025-ynu,
    title = "{YNU}-{HPCC} at {S}em{E}val-2025 Task 6: Using {BERT} Model with {R}-drop for Promise Verification",
    author = "Deng, Dehui  and
      Zhang, You  and
      Wang, Jin  and
      Xu, Dan  and
      Zhang, Xuejie",
    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-1.248/",
    pages = "1905--1911",
    ISBN = "979-8-89176-273-2",
    abstract = "This paper presents our participation in the SemEval-2025 task 6: multinational, multilingual, multi-industry promise verification. The SemEval-2025 Task 6 aims to extract Promise Identification, Supporting Evidence, Clarity of the Promise-Evidence Pair, and Timing for Verification from the commitments made to businesses and governments. Use these data to verify whether companies and governments have fulfilled their commitments. In this task, we participated in the English task, whichincluded analysis of numbers in the text, reading comprehension of the text content and multi-label classification. Our model introduces regularization dropout based on Bert-base to focus on the stability of non-target classes, improve the robustness of the model, and ultimately improve the indicators. Our approach obtained competitive results in subtasks."
}