@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/jlcl-multiple-ingestion/2023.semeval-1.30/",
    doi = "10.18653/v1/2023.semeval-1.30",
    pages = "212--220",
    abstract = "The {\textquotedblleft}Multi-evidence Natural Language Inference forClinical Trial Data{\textquotedblright} 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}."
}