@inproceedings{huang-etal-2023-cpic,
    title = "{CPIC} at {S}em{E}val-2023 Task 7: {GPT}2-Based Model for Multi-evidence Natural Language Inference for Clinical Trial Data",
    author = "Huang, Mingtong  and
      Ren, Junxiang  and
      Liu, Lang  and
      Song, Ruilin  and
      Yin, Wenbo",
    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-1.53",
    doi = "10.18653/v1/2023.semeval-1.53",
    pages = "397--401",
    abstract = "This paper describes our system submitted for SemEval Task 7, Multi-Evidence Natural Language Inference for Clinical Trial Data. The task consists of 2 subtasks. Subtask 1 is to determine the relationships between clinical trial data (CTR) and statements. Subtask 2 is to output a set of supporting facts extracted from the premises with the input of CTR premises and statements. Through experiments, we found that our GPT2-based pre-trained models can obtain good results in Subtask 2. Therefore, we use the GPT2-based pre-trained model to fine-tune Subtask 2. We transform the evidence retrieval task into a binary class task by combining premises and statements as input, and the output is whether the premises and statements match. We obtain a top-5 score in the evaluation phase of Subtask 2.",
}

This paper describes our system submitted for SemEval Task 7, Multi-Evidence Natural Language Inference for Clinical Trial Data. The task consists of 2 subtasks. Subtask 1 is to determine the relationships between clinical trial data (CTR) and statements. Subtask 2 is to output a set of supporting facts extracted from the premises with the input of CTR premises and statements. Through experiments, we found that our GPT2-based pre-trained models can obtain good results in Subtask 2. Therefore, we use the GPT2-based pre-trained model to fine-tune Subtask 2. We transform the evidence retrieval task into a binary class task by combining premises and statements as input, and the output is whether the premises and statements match. We obtain a top-5 score in the evaluation phase of Subtask 2.
Huang, Mingtong Ren, Junxiang Liu, Lang Song, Ruilin Yin, Wenbo
CPIC at SemEval-2023 Task 7: GPT2-Based Model for Multi-evidence Natural Language Inference for Clinical Trial Data