title: FAST READING COMPREHENSION WITH CONVNETS
abstract: 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.
authors: Felix Wu Ni Lao John Blitzer Guandao Yang Kilian Weinberger
hIndex: Kilian Weinberger 66