DATLMedQA: A Data Augmentation and Transfer Learning Based Solution for Medical Question Answering

With the outbreak of COVID-19 that has prompted an increased focus on self-care, more and more people hope to obtain disease knowledge from the Internet. In response to this demand, medical question answering and question generation tasks have become an important part of natural language processing (NLP). However, there are limited samples of medical questions and answers, and the question generation systems cannot fully meet the needs of non-professionals for medical questions. In this research, we propose a BERT medical pretraining model, using GPT-2 for question augmentation and T5-Small for topic extraction, calculating the cosine similarity of the extracted topic and using XGBoost for prediction. With augmentation using GPT-2, the prediction accuracy of our model outperforms the state-of-the-art (SOTA) model performance. Our experiment results demonstrate the outstanding performance of our model in medical question answering and question generation tasks, and its great potential to solve other biomedical question answering challenges.

Automated summary

With the outbreak of COVID-19, there has been a growing demand for disease knowledge, leading to the development of a BERT medical pretraining model that utilizes GPT-2 for question augmentation and T5-Small for topic extraction. By calculating cosine similarity and using XGBoost for prediction, the model demonstrates outstanding performance in medical question answering and generation tasks.