@inproceedings{b97a2e2b8b7a4e0487ed0a86276116cb,
title = "NCUEE-NLP at SemEval-2023 Task 7: Ensemble Biomedical LinkBERT Transformers in Multi-evidence Natural Language Inference for Clinical Trial Data",
abstract = "This study describes the model design of the NCUEE-NLP system for the SemEval-2023 NLI4CT task that focuses on multi-evidence natural language inference for clinical trial data. We use the LinkBERT transformer in the biomedical domain (denoted as BioLinkBERT) as our main system architecture. First, a set of sentences in clinical trial reports is extracted as evidence for premise-statement inference. This identified evidence is then used to determine the inference relation (i.e., entailment or contradiction). Finally, a soft voting ensemble mechanism is applied to enhance the system performance. For Subtask 1 on textual entailment, our best submission had an F1-score of 0.7091, ranking sixth among all 30 participating teams. For Subtask 2 on evidence retrieval, our best result obtained an F1-score of 0.7940, ranking ninth of 19 submissions.",
author = "Chen, {Chao Yi} and Tien, {Kao Yuan} and Cheng, {Yuan Hao} and Lee, {Lung Hao}",
note = "Publisher Copyright: {\textcopyright} 2023 Association for Computational Linguistics.; 17th International Workshop on Semantic Evaluation, SemEval 2023, co-located with the 61st Annual Meeting of the Association for Computational Linguistics, ACL 2023 ; Conference date: 13-07-2023 Through 14-07-2023",
year = "2023",
language = "???core.languages.en_GB???",
series = "17th International Workshop on Semantic Evaluation, SemEval 2023 - Proceedings of the Workshop",
publisher = "Association for Computational Linguistics",
pages = "776--781",
editor = "Ojha, {Atul Kr.} and Dogruoz, {A. Seza} and {Da San Martino}, Giovanni and Madabushi, {Harish Tayyar} and Ritesh Kumar and Elisa Sartori",
booktitle = "17th International Workshop on Semantic Evaluation, SemEval 2023 - Proceedings of the Workshop",
}