  State-of-the-art deep reading comprehension models are dominated by recurrent
neural nets. Their sequential nature is a natural fit for language, but it also
precludes parallelization within an instances and often becomes the bottleneck
for deploying such models to latency critical scenarios. This is particularly
problematic for longer texts. Here we present a convolutional architecture as
an alternative to these recurrent architectures. Using simple dilated
convolutional units in place of recurrent ones, we achieve results comparable
to the state of the art on two question answering tasks, while at the same time
achieving up to two orders of magnitude speedups for question answering.
