FAST READING COMPREHENSION WITH CONVNETS

Felix Wu, Ni Lao, John Blitzer, Guandao Yang, Kilian Weinberger

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.

Keywords: reading comprehension, question answering, CNN, ConvNet, Inference

Code: [ 2 community implementations](https://paperswithcode.com/paper/?openreview=HJRV1ZZAW)

Data: [SQuAD](https://paperswithcode.com/dataset/squad), [TriviaQA](https://paperswithcode.com/dataset/triviaqa)

Community Implementations: [ 6 code implementations](https://www.catalyzex.com/paper/fast-reading-comprehension-with-convnets/code)