@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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T MDC at SemEval-2023 Task 7: Fine-tuning Transformers for Textual Entailment Prediction and Evidence Retrieval in Clinical Trials
%A Bevan, Robert
%A Turbitt, Oisín
%A Aboshokor, Mouhamad
%Y Ojha, Atul Kr.
%Y Doğruöz, A. Seza
%Y Da San Martino, Giovanni
%Y Tayyar Madabushi, Harish
%Y Kumar, Ritesh
%Y Sartori, Elisa
%S Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F bevan-etal-2023-mdc
%X 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.
%R 10.18653/v1/2023.semeval-1.179
%U https://aclanthology.org/2023.semeval-1.179
%U https://doi.org/10.18653/v1/2023.semeval-1.179
%P 1287-1292
Markdown (Informal)
[MDC at SemEval-2023 Task 7: Fine-tuning Transformers for Textual Entailment Prediction and Evidence Retrieval in Clinical Trials](https://aclanthology.org/2023.semeval-1.179) (Bevan et al., SemEval 2023)
ACL