  We present RACE, a new dataset for benchmark evaluation of methods in the
reading comprehension task. Collected from the English exams for middle and
high school Chinese students in the age range between 12 to 18, RACE consists
of near 28,000 passages and near 100,000 questions generated by human experts
(English instructors), and covers a variety of topics which are carefully
designed for evaluating the students' ability in understanding and reasoning.
In particular, the proportion of questions that requires reasoning is much
larger in RACE than that in other benchmark datasets for reading comprehension,
and there is a significant gap between the performance of the state-of-the-art
models (43%) and the ceiling human performance (95%). We hope this new dataset
can serve as a valuable resource for research and evaluation in machine
comprehension. The dataset is freely available at
http://www.cs.cmu.edu/~glai1/data/race/ and the code is available at
https://github.com/qizhex/RACE_AR_baselines.
