@inproceedings{feng-etal-2023-ynu,
    title = "{YNU}-{HPCC} at {S}em{E}val-2023 Task7: Multi-evidence Natural Language Inference for Clinical Trial Data Based a {B}io{BERT} Model",
    author = "Feng, Chao  and
      Wang, Jin  and
      Zhang, Xuejie",
    editor = {Ojha, Atul Kr.  and
      Do{\u{g}}ru{\"o}z, A. Seza  and
      Da San Martino, Giovanni  and
      Tayyar Madabushi, Harish  and
      Kumar, Ritesh  and
      Sartori, Elisa},
    booktitle = "Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.semeval-1.91",
    doi = "10.18653/v1/2023.semeval-1.91",
    pages = "664--670",
    abstract = "This paper describes the system for the YNU-HPCC team in subtask 1 of the SemEval-2023 Task 7: Multi-evidence Natural Language Inference for Clinical Trial Data (NLI4CT). This task requires judging the textual entailment relationship between the given CTR and the statement annotated by the expert annotator. This system is based on the fine-tuned Bi-directional Encoder Representation from Transformers for Biomedical Text Mining (BioBERT) model with supervised contrastive learning and back translation. Supervised contrastive learning is to enhance the classification, and back translation is to enhance the training data. Our system achieved relatively good results on the competition{'}s official leaderboard. The code of this paper is available at \url{https://github.com/facanhe/SemEval-2023-Task7}.",
}

This paper describes the system for the YNU-HPCC team in subtask 1 of the SemEval-2023 Task 7: Multi-evidence Natural Language Inference for Clinical Trial Data (NLI4CT). This task requires judging the textual entailment relationship between the given CTR and the statement annotated by the expert annotator. This system is based on the fine-tuned Bi-directional Encoder Representation from Transformers for Biomedical Text Mining (BioBERT) model with supervised contrastive learning and back translation. Supervised contrastive learning is to enhance the classification, and back translation is to enhance the training data. Our system achieved relatively good results on the competition{'}s official leaderboard. The code of this paper is available at \url{https://github.com/facanhe/SemEval-2023-Task7}.
Feng, Chao Wang, Jin Zhang, Xuejie
YNU-HPCC at SemEval-2023 Task7: Multi-evidence Natural Language Inference for Clinical Trial Data Based a BioBERT Model