@inproceedings{alameldin-williamson-2023-clemson,
    title = "Clemson {NLP} at {S}em{E}val-2023 Task 7: Applying {G}ator{T}ron to Multi-Evidence Clinical {NLI}",
    author = "Alameldin, Ahamed  and
      Williamson, Ashton",
    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://preview.aclanthology.org/ingest_wac_2008/2023.semeval-1.220/",
    doi = "10.18653/v1/2023.semeval-1.220",
    pages = "1598--1602",
    abstract = "This paper presents our system descriptions for SemEval 2023-Task 7: Multi-evidence Natural Language Inference for Clinical Trial Data sub-tasks one and two. Provided with a collection of Clinical Trial Reports (CTRs) and corresponding expert-annotated claim statements, sub-task one involves determining an inferential relationship between the statement and CTR premise: contradiction or entailment. Sub-task two involves retrieving evidence from the CTR which is necessary to determine the entailment in sub-task one. For sub-task two we employ a recent transformer-based language model pretrained on biomedical literature, which we domain-adapt on a set of clinical trial reports. For sub-task one, we take an ensemble approach in which we leverage the evidence retrieval model from sub-task two to extract relevant sections, which are then passed to a second model of equivalent architecture to determine entailment. Our system achieves a ranking of seventh on sub-task one with an F1-score of 0.705 and sixth on sub-task two with an F1-score of 0.806. In addition, we find that the high rate of success of language models on this dataset may be partially attributable to the existence of annotation artifacts."
}