@inproceedings{fahfouh-etal-2024-usmba,
title = "{USMBA}-{NLP} at {S}em{E}val-2024 Task 2: Safe Biomedical Natural Language Inference for Clinical Trials using Bert",
author = "Fahfouh, Anass and
Benlahbib, Abdessamad and
Riffi, Jamal and
Tairi, Hamid",
editor = {Ojha, Atul Kr. and
Do{\u{g}}ru{\"o}z, A. Seza and
Tayyar Madabushi, Harish and
Da San Martino, Giovanni and
Rosenthal, Sara and
Ros{\'a}, Aiala},
booktitle = "Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024)",
month = jun,
year = "2024",
address = "Mexico City, Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.semeval-1.66",
doi = "10.18653/v1/2024.semeval-1.66",
pages = "432--436",
abstract = "This paper presents the application of BERT inSemEval 2024 Task 2, Safe Biomedical Natu-ral Language Inference for Clinical Trials. Themain objectives of this task were: First, to in-vestigate the consistency of BERT in its rep-resentation of semantic phenomena necessaryfor complex inference in clinical NLI settings.Second, to investigate the ability of BERT toperform faithful reasoning, i.e., make correctpredictions for the correct reasons. The submit-ted model is fine-tuned on the NLI4CT dataset,which is enhanced with a novel contrast set,using binary cross entropy loss.",
}
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%0 Conference Proceedings
%T USMBA-NLP at SemEval-2024 Task 2: Safe Biomedical Natural Language Inference for Clinical Trials using Bert
%A Fahfouh, Anass
%A Benlahbib, Abdessamad
%A Riffi, Jamal
%A Tairi, Hamid
%Y Ojha, Atul Kr.
%Y Doğruöz, A. Seza
%Y Tayyar Madabushi, Harish
%Y Da San Martino, Giovanni
%Y Rosenthal, Sara
%Y Rosá, Aiala
%S Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024)
%D 2024
%8 June
%I Association for Computational Linguistics
%C Mexico City, Mexico
%F fahfouh-etal-2024-usmba
%X This paper presents the application of BERT inSemEval 2024 Task 2, Safe Biomedical Natu-ral Language Inference for Clinical Trials. Themain objectives of this task were: First, to in-vestigate the consistency of BERT in its rep-resentation of semantic phenomena necessaryfor complex inference in clinical NLI settings.Second, to investigate the ability of BERT toperform faithful reasoning, i.e., make correctpredictions for the correct reasons. The submit-ted model is fine-tuned on the NLI4CT dataset,which is enhanced with a novel contrast set,using binary cross entropy loss.
%R 10.18653/v1/2024.semeval-1.66
%U https://aclanthology.org/2024.semeval-1.66
%U https://doi.org/10.18653/v1/2024.semeval-1.66
%P 432-436
Markdown (Informal)
[USMBA-NLP at SemEval-2024 Task 2: Safe Biomedical Natural Language Inference for Clinical Trials using Bert](https://aclanthology.org/2024.semeval-1.66) (Fahfouh et al., SemEval 2024)
ACL