@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://aclanthology.org/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}.",
}
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<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 - https://github.com/volosincu/FII_Smart__Semeval2023.</abstract>
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%0 Conference Proceedings
%T FII SMART at SemEval 2023 Task7: Multi-evidence Natural Language Inference for Clinical Trial Data
%A Volosincu, Mihai
%A Lupu, Cosmin
%A Trandabat, Diana
%A Gifu, Daniela
%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 volosincu-etal-2023-fii
%X 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 - https://github.com/volosincu/FII_Smart__Semeval2023.
%R 10.18653/v1/2023.semeval-1.30
%U https://aclanthology.org/2023.semeval-1.30
%U https://doi.org/10.18653/v1/2023.semeval-1.30
%P 212-220
Markdown (Informal)
[FII SMART at SemEval 2023 Task7: Multi-evidence Natural Language Inference for Clinical Trial Data](https://aclanthology.org/2023.semeval-1.30) (Volosincu et al., SemEval 2023)
ACL