@inproceedings{volosincu-etal-2023-fii,
    title = "{FII} {SMART} at {S}em{E}val 2023 Task7: Multi-evidence Natural Language Inference for Clinical Trial Data",
    author = "Volosincu, Mihai  and
      Lupu, Cosmin  and
      Trandabat, Diana  and
      Gifu, Daniela",
    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/moar-dois/2023.semeval-1.30/",
    doi = "10.18653/v1/2023.semeval-1.30",
    pages = "212--220",
    abstract = "The ``Multi-evidence Natural Language Inference forClinical Trial Data'' task at SemEval 2023competition focuses on extracting essentialinformation on clinical trial data, by posing twosubtasks on textual entailment and evidence retrieval. In the context of SemEval, we present a comparisonbetween a method based on the BioBERT model anda CNN model. The task is based on a collection ofbreast cancer Clinical Trial Reports (CTRs),statements, explanations, and labels annotated bydomain expert annotators. We achieved F1 scores of0.69 for determining the inference relation(entailment vs contradiction) between CTR -statement pairs. The implementation of our system ismade available via Github - \url{https://github.com/volosincu/FII_Smart__Semeval2023}."
}