@inproceedings{takehana-etal-2023-stanford,
title = "{S}tanford {ML}ab at {S}em{E}val 2023 Task 7: Neural Methods for Clinical Trial Report {NLI}",
author = "Takehana, Conner and
Lim, Dylan and
Kurtulus, Emirhan and
Iyer, Ramya and
Tanimura, Ellie and
Aggarwal, Pankhuri and
Cantillon, Molly and
Yu, Alfred and
Khan, Sarosh and
Chi, Nathan",
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-1.245",
doi = "10.18653/v1/2023.semeval-1.245",
pages = "1769--1775",
abstract = "We present a system for natural language inference in breast cancer clinical trial reports, as framed by SemEval 2023 Task 7: Multi-evidence Natural Language Inference for Clinical Trial Data. In particular, we propose a suite of techniques for two related inference subtasks: entailment and evidence retrieval. The purpose of the textual entailment identification subtask is to determine the inference relation (either entailment or contradiction) between given statement pairs, while the goal of the evidence retrieval task is to identify a set of sentences that support this inference relation. To this end, we propose fine-tuning Bio+Clinical BERT, a BERT-based model pre-trained on clinical data. Along with presenting our system, we analyze our architectural decisions in the context of our model{'}s accuracy and conduct an error analysis. Overall, our system ranked 20 / 30 on the entailment subtask.",
}
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<abstract>We present a system for natural language inference in breast cancer clinical trial reports, as framed by SemEval 2023 Task 7: Multi-evidence Natural Language Inference for Clinical Trial Data. In particular, we propose a suite of techniques for two related inference subtasks: entailment and evidence retrieval. The purpose of the textual entailment identification subtask is to determine the inference relation (either entailment or contradiction) between given statement pairs, while the goal of the evidence retrieval task is to identify a set of sentences that support this inference relation. To this end, we propose fine-tuning Bio+Clinical BERT, a BERT-based model pre-trained on clinical data. Along with presenting our system, we analyze our architectural decisions in the context of our model’s accuracy and conduct an error analysis. Overall, our system ranked 20 / 30 on the entailment subtask.</abstract>
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%0 Conference Proceedings
%T Stanford MLab at SemEval 2023 Task 7: Neural Methods for Clinical Trial Report NLI
%A Takehana, Conner
%A Lim, Dylan
%A Kurtulus, Emirhan
%A Iyer, Ramya
%A Tanimura, Ellie
%A Aggarwal, Pankhuri
%A Cantillon, Molly
%A Yu, Alfred
%A Khan, Sarosh
%A Chi, Nathan
%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 takehana-etal-2023-stanford
%X We present a system for natural language inference in breast cancer clinical trial reports, as framed by SemEval 2023 Task 7: Multi-evidence Natural Language Inference for Clinical Trial Data. In particular, we propose a suite of techniques for two related inference subtasks: entailment and evidence retrieval. The purpose of the textual entailment identification subtask is to determine the inference relation (either entailment or contradiction) between given statement pairs, while the goal of the evidence retrieval task is to identify a set of sentences that support this inference relation. To this end, we propose fine-tuning Bio+Clinical BERT, a BERT-based model pre-trained on clinical data. Along with presenting our system, we analyze our architectural decisions in the context of our model’s accuracy and conduct an error analysis. Overall, our system ranked 20 / 30 on the entailment subtask.
%R 10.18653/v1/2023.semeval-1.245
%U https://aclanthology.org/2023.semeval-1.245
%U https://doi.org/10.18653/v1/2023.semeval-1.245
%P 1769-1775
Markdown (Informal)
[Stanford MLab at SemEval 2023 Task 7: Neural Methods for Clinical Trial Report NLI](https://aclanthology.org/2023.semeval-1.245) (Takehana et al., SemEval 2023)
ACL
- Conner Takehana, Dylan Lim, Emirhan Kurtulus, Ramya Iyer, Ellie Tanimura, Pankhuri Aggarwal, Molly Cantillon, Alfred Yu, Sarosh Khan, and Nathan Chi. 2023. Stanford MLab at SemEval 2023 Task 7: Neural Methods for Clinical Trial Report NLI. In Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023), pages 1769–1775, Toronto, Canada. Association for Computational Linguistics.