@inproceedings{bevan-etal-2023-mdc,
    title = "{MDC} at {S}em{E}val-2023 Task 7: Fine-tuning Transformers for Textual Entailment Prediction and Evidence Retrieval in Clinical Trials",
    author = "Bevan, Robert  and
      Turbitt, Ois{\'\i}n  and
      Aboshokor, Mouhamad",
    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-1.179",
    doi = "10.18653/v1/2023.semeval-1.179",
    pages = "1287--1292",
    abstract = "We present our entry to the Multi-evidence Natural Language Inference for Clinical Trial Datatask at SemEval 2023. We submitted entries forboth the evidence retrieval and textual entailment sub-tasks. For the evidence retrieval task,we fine-tuned the PubMedBERT transformermodel to extract relevant evidence from clinicaltrial data given a hypothesis concerning either asingle clinical trial or pair of clinical trials. Ourbest performing model achieved an F1 scoreof 0.804. For the textual entailment task, inwhich systems had to predict whether a hypothesis about either a single clinical trial or pair ofclinical trials is true or false, we fine-tuned theBioLinkBERT transformer model. We passedour evidence retrieval model{'}s output into ourtextual entailment model and submitted its output for the evaluation. Our best performingmodel achieved an F1 score of 0.695.",
}

We present our entry to the Multi-evidence Natural Language Inference for Clinical Trial Datatask at SemEval 2023. We submitted entries forboth the evidence retrieval and textual entailment sub-tasks. For the evidence retrieval task,we fine-tuned the PubMedBERT transformermodel to extract relevant evidence from clinicaltrial data given a hypothesis concerning either asingle clinical trial or pair of clinical trials. Ourbest performing model achieved an F1 scoreof 0.804. For the textual entailment task, inwhich systems had to predict whether a hypothesis about either a single clinical trial or pair ofclinical trials is true or false, we fine-tuned theBioLinkBERT transformer model. We passedour evidence retrieval model{'}s output into ourtextual entailment model and submitted its output for the evaluation. Our best performingmodel achieved an F1 score of 0.695.
Bevan, Robert Turbitt, Ois{\'\i}n Aboshokor, Mouhamad
MDC at SemEval-2023 Task 7: Fine-tuning Transformers for Textual Entailment Prediction and Evidence Retrieval in Clinical Trials