@inproceedings{alissa-abdullah-2023-just,
    title = "{JUST}-{KM} at {S}em{E}val-2023 Task 7: Multi-evidence Natural Language Inference using Role-based Double Roberta-Large",
    author = "Alissa, Kefah  and
      Abdullah, Malak",
    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-1.61",
    doi = "10.18653/v1/2023.semeval-1.61",
    pages = "447--452",
    abstract = "In recent years, there has been a vast increase in the available clinical data. Variant Deep learning techniques are used to enhance the retrieval and interpretation of these data. This task deployed Natural language inference (NLI) in Clinical Trial Reports (CTRs) to provide individualized care that is supported by evidence. A collection of breast cancer clinical trial records, statements, annotations, and labels from experienced domain experts. NLI presents a chance to advance the widespread understanding and retrieval of medical evidence, leading to significant improvements in connecting the most recent evidence to personalized care. The primary objective is to identify the inference relationship (entailment or contradiction) between pairs of clinical trial records and statements. In this research, we used different transformer-based models, and The proposed model, {``}Role-based Double Roberta-Large,{''} achieved the best result on the testing dataset with F1-score equal to 67.0{\%}",
}

In recent years, there has been a vast increase in the available clinical data. Variant Deep learning techniques are used to enhance the retrieval and interpretation of these data. This task deployed Natural language inference (NLI) in Clinical Trial Reports (CTRs) to provide individualized care that is supported by evidence. A collection of breast cancer clinical trial records, statements, annotations, and labels from experienced domain experts. NLI presents a chance to advance the widespread understanding and retrieval of medical evidence, leading to significant improvements in connecting the most recent evidence to personalized care. The primary objective is to identify the inference relationship (entailment or contradiction) between pairs of clinical trial records and statements. In this research, we used different transformer-based models, and The proposed model, {``}Role-based Double Roberta-Large,{''} achieved the best result on the testing dataset with F1-score equal to 67.0{\%}
Alissa, Kefah Abdullah, Malak
JUST-KM at SemEval-2023 Task 7: Multi-evidence Natural Language Inference using Role-based Double Roberta-Large