  We present Native Chinese Reader (NCR), a new machine reading comprehension
(MRC) dataset with particularly long articles in both modern and classical
Chinese. NCR is collected from the exam questions for the Chinese course in
China's high schools, which are designed to evaluate the language proficiency
of native Chinese youth. Existing Chinese MRC datasets are either
domain-specific or focusing on short contexts of a few hundreds of characters
in modern Chinese only. By contrast, NCR contains 8390 documents with an
average length of 1024 characters covering a wide range of Chinese writing
styles, including modern articles, classical literature and classical poetry. A
total of 20477 questions on these documents also require strong reasoning
abilities and common sense to figure out the correct answers. We implemented
multiple baseline models using popular Chinese pre-trained models and
additionally launched an online competition using our dataset to examine the
limit of current methods. The best model achieves 59% test accuracy while human
evaluation shows an average accuracy of 79%, which indicates a significant
performance gap between current MRC models and native Chinese speakers. We
release the dataset at https://sites.google.com/view/native-chinese-reader/.
