  In this paper, we present a new verification style reading comprehension
dataset named VGaokao from Chinese Language tests of Gaokao. Different from
existing efforts, the new dataset is originally designed for native speakers'
evaluation, thus requiring more advanced language understanding skills. To
address the challenges in VGaokao, we propose a novel Extract-Integrate-Compete
approach, which iteratively selects complementary evidence with a novel query
updating mechanism and adaptively distills supportive evidence, followed by a
pairwise competition to push models to learn the subtle difference among
similar text pieces. Experiments show that our methods outperform various
baselines on VGaokao with retrieved complementary evidence, while having the
merits of efficiency and explainability. Our dataset and code are released for
further research.
