@inproceedings{lee-etal-2023-ncuee,
    title = "{NCUEE}-{NLP} at {S}em{E}val-2023 Task 8: Identifying Medical Causal Claims and Extracting {PIO} Frames Using the Transformer Models",
    author = "Lee, Lung-Hao  and
      Cheng, Yuan-Hao  and
      Yang, Jen-Hao  and
      Tien, Kao-Yuan",
    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-1.42",
    doi = "10.18653/v1/2023.semeval-1.42",
    pages = "312--317",
    abstract = "This study describes the model design of the NCUEE-NLP system for the SemEval-2023 Task 8. We use the pre-trained transformer models and fine-tune the task datasets to identify medical causal claims and extract population, intervention, and outcome elements in a Reddit post when a claim is given. Our best system submission for the causal claim identification subtask achieved a F1-score of 70.15{\%}. Our best submission for the PIO frame extraction subtask achieved F1-scores of 37.78{\%} for Population class, 43.58{\%} for Intervention class, and 30.67{\%} for Outcome class, resulting in a macro-averaging F1-score of 37.34{\%}. Our system evaluation results ranked second position among all participating teams.",
}

This study describes the model design of the NCUEE-NLP system for the SemEval-2023 Task 8. We use the pre-trained transformer models and fine-tune the task datasets to identify medical causal claims and extract population, intervention, and outcome elements in a Reddit post when a claim is given. Our best system submission for the causal claim identification subtask achieved a F1-score of 70.15{\%}. Our best submission for the PIO frame extraction subtask achieved F1-scores of 37.78{\%} for Population class, 43.58{\%} for Intervention class, and 30.67{\%} for Outcome class, resulting in a macro-averaging F1-score of 37.34{\%}. Our system evaluation results ranked second position among all participating teams.
Lee, Lung-Hao Cheng, Yuan-Hao Yang, Jen-Hao Tien, Kao-Yuan
NCUEE-NLP at SemEval-2023 Task 8: Identifying Medical Causal Claims and Extracting PIO Frames Using the Transformer Models