SubmissionNumber#=%=#117
FinalPaperTitle#=%=#YNU-HPCC at SemEval-2024 Task 2: Applying DeBERTa-v3-large to Safe Biomedical Natural Language Inference for Clinical Trials
ShortPaperTitle#=%=#
NumberOfPages#=%=#7
CopyrightSigned#=%=#Rengui Zhang
JobTitle#==#
Organization#==#School of Information Science and Engineering
Yunnan University
Kunming, China
Abstract#==#This paper describes the system for the YNU-HPCC team for SemEval2024 Task 2, focusing on Safe Biomedical Natural Language Inference for Clinical Trials. The core challenge of this task lies in discerning the textual entailment relationship between Clinical Trial Reports (CTR) and statements annotated by expert annotators, including the necessity to infer the relationships in texts subjected to semantic interventions accurately. Our approach leverages a fine-tuned DeBERTa-v3-large model augmented with supervised contrastive learning and back-translation techniques. Supervised contrastive learning aims to bolster classification ac-curacy while back-translation enriches the diversity and quality of our training corpus. Our method achieves a decent F1 score. However, the results also indicate a need for further en-hancements in the system's capacity for deep semantic comprehension, highlighting areas for future refinement. The code of this paper is available at:https://github.com/RGTnuw/RG_YNU-HPCC-at-Semeval2024-Task2.
Author{1}{Firstname}#=%=#Rengui
Author{1}{Lastname}#=%=#Zhang
Author{1}{Username}#=%=#rgzhang
Author{1}{Email}#=%=#zrg@mail.ynu.edu.cn
Author{1}{Affiliation}#=%=#Yunnan University
Author{2}{Firstname}#=%=#Jin
Author{2}{Lastname}#=%=#Wang
Author{2}{Username}#=%=#wangjin0818
Author{2}{Email}#=%=#wangjin@ynu.edu.cn
Author{2}{Affiliation}#=%=#Yunnan University
Author{3}{Firstname}#=%=#Xuejie
Author{3}{Lastname}#=%=#Zhang
Author{3}{Username}#=%=#xjzhang
Author{3}{Email}#=%=#xjzhang@ynu.edu.cn
Author{3}{Affiliation}#=%=#Yunnan University

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