  We present a dual contribution to the task of machine reading-comprehension:
a technique for creating large-sized machine-comprehension (MC) datasets using
paragraph-vector models; and a novel, hybrid neural-network architecture that
combines the representation power of recurrent neural networks with the
discriminative power of fully-connected multi-layered networks. We use the
MC-dataset generation technique to build a dataset of around 2 million
examples, for which we empirically determine the high-ceiling of human
performance (around 91% accuracy), as well as the performance of a variety of
computer models. Among all the models we have experimented with, our hybrid
neural-network architecture achieves the highest performance (83.2% accuracy).
The remaining gap to the human-performance ceiling provides enough room for
future model improvements.
