A Medical Question Classification Approach Based on Prompt Tuning and Contrastive Learning (S)

COVID-19 has profoundly impacted people's lives, and people are more concerned about medical and health issues, so it is essential to design an efficient method for classifying medical questions.Fine-tuning paradigms based on pre-trained language models have proven effective in recent years.However, PLMs based on fine-tuning paradigms are poorly robust, and there is a gap between the pre-training phase and the downstream task form, resulting in PLMs that cannot use the rich latent knowledge in downstream tasks.We propose a medical question classification method that combines prompt fine-tuning and contrastive learning and uses the large-scale knowledge graph enhancement model ERNIE 3.0 as a feature extractor to address both problems.Our approach utilizes an additional prompt template to enable PLM to unleash the potential in specific tasks and uses a contrast sample strategy to alleviate the problem of confusable samples that are difficult to distinguish.Experiments on a medical question classification dataset show that the method achieves an accuracy of 93.65 percent, with better metrics than recent work.