DKE-Research at SemEval-2024 Task 2: Incorporating Data Augmentation with Generative Models and Biomedical Knowledge to Enhance Inference Robustness

arXiv:2404.09206v1 Announce Type: new
Abstract: Safe and reliable natural language inference is critical for extracting insights from clinical trial reports but poses challenges due to biases in large pre-trained language models. This paper presents a novel data augmentation technique to improve model robustness for biomedical natural language inference in clinical trials. By generating synthetic examples through semantic perturbations and domain-specific vocabulary replacement and adding a new task for numerical and quantitative reasoning, we introduce greater diversity and reduce shortcut learning. …

abstract arxiv augmentation biases biomedical challenges clinical clinical trial cs.cl data generative generative models inference insights knowledge language language models natural natural language novel paper reports research robustness safe type