@inproceedings{zhang-etal-2024-ynu,
    title = "{YNU}-{HPCC} at {S}em{E}val-2024 Task 2: Applying {D}e{BERT}a-v3-large to Safe Biomedical Natural Language Inference for Clinical Trials",
    author = "Zhang, Rengui  and
      Wang, Jin  and
      Zhang, Xuejie",
    editor = {Ojha, Atul Kr.  and
      Do{\u{g}}ru{\"o}z, A. Seza  and
      Tayyar Madabushi, Harish  and
      Da San Martino, Giovanni  and
      Rosenthal, Sara  and
      Ros{\'a}, Aiala},
    booktitle = "Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024)",
    month = jun,
    year = "2024",
    address = "Mexico City, Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/Add-Cong-Liu-Florida-Atlantic-University-author-id/2024.semeval-1.112/",
    doi = "10.18653/v1/2024.semeval-1.112",
    pages = "785--791",
    abstract = "This paper describes the system for the YNU-HPCC team for SemEval2024 Task 2, focusing on Safe Biomedical Natural Language Inference for Clinical Trials. The core challenge of this task lies in discerning the textual entailment relationship between Clinical Trial Reports (CTR) and statements annotated by expert annotators, including the necessity to infer the relationships in texts subjected to semantic interventions accurately. Our approach leverages a fine-tuned DeBERTa-v3-large model augmented with supervised contrastive learning and back-translation techniques. Supervised contrastive learning aims to bolster classification ac-curacy while back-translation enriches the diversity and quality of our training corpus. Our method achieves a decent F1 score. However, the results also indicate a need for further en-hancements in the system`s capacity for deep semantic comprehension, highlighting areas for future refinement. The code of this paper is available at:https://github.com/RGTnuw/RG{\_}YNU-HPCC-at-Semeval2024-Task2."
}