NCUEE-NLP at SemEval-2023 Task 7: Ensemble Biomedical LinkBERT Transformers in Multi-evidence Natural Language Inference for Clinical Trial Data
Chao-Yi Chen, Kao-Yuan Tien, Yuan-Hao Cheng, Lung‐Hao Lee · 2023
This study describes the model design of the NCUEE-NLP system for the SemEval-2023 NLI4CT task that focuses on multievidence 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.