MLECQA: A Chinese Multi-Choice Biomedical Question Answering Dataset

Question Answering (QA) has been successfully applied in scenarios of human-computer interaction such as chatbots and search engines. However, for the specific biomedical domain, QA systems are still immature due to expert-annotated datasets being limited by category and scale. In this paper, we present MLEC-QA, the largest-scale Chinese multi-choice biomedical QA dataset, collected from the National Medical Licensing Examination in China. The dataset is composed of five subsets with 136,236 biomedical multi-choice questions with extra materials (images or tables) annotated by human experts, and first covers the following biomedical sub-fields: Clinic, Stomatology, Public Health, Traditional Chinese Medicine, and Traditional Chinese Medicine Combined with Western Medicine. We implement eight representative control methods and open-domain QA methods as baselines. Experimental results demonstrate that even the current best model can only achieve accuracies between 40 to 55 on five subsets, especially performing poorly on questions that require sophisticated reasoning ability. We hope the release of the MLEC-QA dataset can serve as a valuable resource for research and evaluation in open-domain QA, and also make advances for biomedical QA systems.