@inproceedings{zhao-etal-2023-hw,
    title = "{HW}-{TSC} at {S}em{E}val-2023 Task 7: Exploring the Natural Language Inference Capabilities of {C}hat{GPT} and Pre-trained Language Model for Clinical Trial",
    author = "Zhao, Xiaofeng  and
      Zhang, Min  and
      Ma, Miaomiao  and
      Su, Chang  and
      Liu, Yilun  and
      Wang, Minghan  and
      Qiao, Xiaosong  and
      Guo, Jiaxin  and
      Li, Yinglu  and
      Ma, Wenbing",
    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-1.221",
    doi = "10.18653/v1/2023.semeval-1.221",
    pages = "1603--1608",
    abstract = "In this paper, we describe the multi strategy system for SemEval-2022 Task 7, This task aims to determine whether a given statement is supported by one or two Clinical Trial reports, and to identify evidence that supports the statement. This is a task that requires high natural language inference capabilities. In Subtask 1, we compare our strategy based on prompt learning and ChatGPT with a baseline constructed using BERT in zero-shot setting, and validate the effectiveness of our strategy. In Subtask 2, we fine-tune DeBERTaV3 for classification without relying on the results from Subtask 1, and we observe that early stopping can effectively prevent model overfitting, which performs well in Subtask 2. In addition, we did not use any ensemble strategies. Ultimately, we achieved the 10th place in Subtask 1 and the 2nd place in Subtask 2.",
}

In this paper, we describe the multi strategy system for SemEval-2022 Task 7, This task aims to determine whether a given statement is supported by one or two Clinical Trial reports, and to identify evidence that supports the statement. This is a task that requires high natural language inference capabilities. In Subtask 1, we compare our strategy based on prompt learning and ChatGPT with a baseline constructed using BERT in zero-shot setting, and validate the effectiveness of our strategy. In Subtask 2, we fine-tune DeBERTaV3 for classification without relying on the results from Subtask 1, and we observe that early stopping can effectively prevent model overfitting, which performs well in Subtask 2. In addition, we did not use any ensemble strategies. Ultimately, we achieved the 10th place in Subtask 1 and the 2nd place in Subtask 2.
Zhao, Xiaofeng Zhang, Min Ma, Miaomiao Su, Chang Liu, Yilun Wang, Minghan Qiao, Xiaosong Guo, Jiaxin Li, Yinglu Ma, Wenbing
HW-TSC at SemEval-2023 Task 7: Exploring the Natural Language Inference Capabilities of ChatGPT and Pre-trained Language Model for Clinical Trial