Kao-Yuan Tien


2023

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NCUEE-NLP at SemEval-2023 Task 8: Identifying Medical Causal Claims and Extracting PIO Frames Using the Transformer Models
Lung-Hao Lee | Yuan-Hao Cheng | Jen-Hao Yang | Kao-Yuan Tien
Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)

This study describes the model design of the NCUEE-NLP system for the SemEval-2023 Task 8. We use the pre-trained transformer models and fine-tune the task datasets to identify medical causal claims and extract population, intervention, and outcome elements in a Reddit post when a claim is given. Our best system submission for the causal claim identification subtask achieved a F1-score of 70.15%. Our best submission for the PIO frame extraction subtask achieved F1-scores of 37.78% for Population class, 43.58% for Intervention class, and 30.67% for Outcome class, resulting in a macro-averaging F1-score of 37.34%. Our system evaluation results ranked second position among all participating teams.

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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
Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)

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.