Towards Explainable Medical Machine Reading Comprehension With Rationale Generation

Machine reading comprehension (MRC) has achieved remarkable progress in the open domain in recent years, mainly due to large-scale pre-trained language models. However, it performs much worse in specific domains such as the medical field due to the lack of extensive training data and professional structural knowledge neglect. Besides, the opacity of the large-scale neural language model also limits its practical utility. As an effort, we first collect a medical machine reading comprehension question dataset with experts' annotations for the National Licensed Pharmacist Examination in China. It is a challenging medical examination with a passing rate of less than 14.2% in 2018. Then we propose a novel reading comprehension model eKMQA, which can fully exploit the structural medical knowledge (i.e., medical knowledge graph) and the reference medical plain text (i.e., text snippets retrieved from reference books). Meanwhile, it improves the explainability of the model by leveraging the knowledge entailed in explanations as an additional distillation signal for more efficient learning. The experimental results demonstrate that our method outperforms existing competitive models with a large margin, which improves prediction accuracy on the test set and can generate reasonable free-text rationales for its predictions even with a small-scale training corpus.