Oisn Turbitt
2023
MDC at SemEval-2023 Task 7: Fine-tuning Transformers for Textual Entailment Prediction and Evidence Retrieval in Clinical Trials
Robert Bevan
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Oisn Turbitt
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Mouhamad Aboshokor
Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)
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.
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