@inproceedings{huang-etal-2023-cpic,
title = "{CPIC} at {S}em{E}val-2023 Task 7: {GPT}2-Based Model for Multi-evidence Natural Language Inference for Clinical Trial Data",
author = "Huang, Mingtong and
Ren, Junxiang and
Liu, Lang and
Song, Ruilin and
Yin, Wenbo",
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-1.53",
doi = "10.18653/v1/2023.semeval-1.53",
pages = "397--401",
abstract = "This paper describes our system submitted for SemEval Task 7, Multi-Evidence Natural Language Inference for Clinical Trial Data. The task consists of 2 subtasks. Subtask 1 is to determine the relationships between clinical trial data (CTR) and statements. Subtask 2 is to output a set of supporting facts extracted from the premises with the input of CTR premises and statements. Through experiments, we found that our GPT2-based pre-trained models can obtain good results in Subtask 2. Therefore, we use the GPT2-based pre-trained model to fine-tune Subtask 2. We transform the evidence retrieval task into a binary class task by combining premises and statements as input, and the output is whether the premises and statements match. We obtain a top-5 score in the evaluation phase of Subtask 2.",
}
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<abstract>This paper describes our system submitted for SemEval Task 7, Multi-Evidence Natural Language Inference for Clinical Trial Data. The task consists of 2 subtasks. Subtask 1 is to determine the relationships between clinical trial data (CTR) and statements. Subtask 2 is to output a set of supporting facts extracted from the premises with the input of CTR premises and statements. Through experiments, we found that our GPT2-based pre-trained models can obtain good results in Subtask 2. Therefore, we use the GPT2-based pre-trained model to fine-tune Subtask 2. We transform the evidence retrieval task into a binary class task by combining premises and statements as input, and the output is whether the premises and statements match. We obtain a top-5 score in the evaluation phase of Subtask 2.</abstract>
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%0 Conference Proceedings
%T CPIC at SemEval-2023 Task 7: GPT2-Based Model for Multi-evidence Natural Language Inference for Clinical Trial Data
%A Huang, Mingtong
%A Ren, Junxiang
%A Liu, Lang
%A Song, Ruilin
%A Yin, Wenbo
%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 huang-etal-2023-cpic
%X This paper describes our system submitted for SemEval Task 7, Multi-Evidence Natural Language Inference for Clinical Trial Data. The task consists of 2 subtasks. Subtask 1 is to determine the relationships between clinical trial data (CTR) and statements. Subtask 2 is to output a set of supporting facts extracted from the premises with the input of CTR premises and statements. Through experiments, we found that our GPT2-based pre-trained models can obtain good results in Subtask 2. Therefore, we use the GPT2-based pre-trained model to fine-tune Subtask 2. We transform the evidence retrieval task into a binary class task by combining premises and statements as input, and the output is whether the premises and statements match. We obtain a top-5 score in the evaluation phase of Subtask 2.
%R 10.18653/v1/2023.semeval-1.53
%U https://aclanthology.org/2023.semeval-1.53
%U https://doi.org/10.18653/v1/2023.semeval-1.53
%P 397-401
Markdown (Informal)
[CPIC at SemEval-2023 Task 7: GPT2-Based Model for Multi-evidence Natural Language Inference for Clinical Trial Data](https://aclanthology.org/2023.semeval-1.53) (Huang et al., SemEval 2023)
ACL