  Cloze-style reading comprehension has been a popular task for measuring the
progress of natural language understanding in recent years. In this paper, we
design a novel multi-perspective framework, which can be seen as the joint
training of heterogeneous experts and aggregate context information from
different perspectives. Each perspective is modeled by a simple aggregation
module. The outputs of multiple aggregation modules are fed into a one-timestep
pointer network to get the final answer. At the same time, to tackle the
problem of insufficient labeled data, we propose an efficient sampling
mechanism to automatically generate more training examples by matching the
distribution of candidates between labeled and unlabeled data. We conduct our
experiments on a recently released cloze-test dataset CLOTH (Xie et al., 2017),
which consists of nearly 100k questions designed by professional teachers.
Results show that our method achieves new state-of-the-art performance over
previous strong baselines.
