@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{\%}",
}
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%0 Conference Proceedings
%T JUST-KM at SemEval-2023 Task 7: Multi-evidence Natural Language Inference using Role-based Double Roberta-Large
%A Alissa, Kefah
%A Abdullah, Malak
%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 alissa-abdullah-2023-just
%X 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%
%R 10.18653/v1/2023.semeval-1.61
%U https://aclanthology.org/2023.semeval-1.61
%U https://doi.org/10.18653/v1/2023.semeval-1.61
%P 447-452
Markdown (Informal)
[JUST-KM at SemEval-2023 Task 7: Multi-evidence Natural Language Inference using Role-based Double Roberta-Large](https://aclanthology.org/2023.semeval-1.61) (Alissa & Abdullah, SemEval 2023)
ACL