@inproceedings{neves-2023-bf3r,
    title = "{B}f3{R} at {S}em{E}val-2023 Task 7: a text similarity model for textual entailment and evidence retrieval in clinical trials and animal studies",
    author = "Neves, Mariana",
    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/add-emnlp-2024-awards/2023.semeval-1.17/",
    doi = "10.18653/v1/2023.semeval-1.17",
    pages = "125--129",
    abstract = "We describe our participation on the Multi-evidence Natural Language Inference for Clinical Trial Data (NLI4CT) of SemEval`23. The organizers provided a collection of clinical trials as training data and a set of statements, which can be related to either a single trial or to a comparison of two trials. The task consisted of two sub-tasks: (i) textual entailment (Task 1) for predicting whether the statement is supported (Entailment) or not (Contradiction) by the corresponding trial(s); and (ii) evidence retrieval (Task 2) for selecting the evidences (sentences in the trials) that support the decision made for Task 1. We built a model based on a sentence-based BERT similarity model which was pre-trained on ClinicalBERT embeddings. Our best results on the official test sets were f-scores of 0.64 and 0.67 for Tasks 1 and 2, respectively."
}