  We present a framework for question answering that can efficiently scale to
longer documents while maintaining or even improving performance of
state-of-the-art models. While most successful approaches for reading
comprehension rely on recurrent neural networks (RNNs), running them over long
documents is prohibitively slow because it is difficult to parallelize over
sequences. Inspired by how people first skim the document, identify relevant
parts, and carefully read these parts to produce an answer, we combine a
coarse, fast model for selecting relevant sentences and a more expensive RNN
for producing the answer from those sentences. We treat sentence selection as a
latent variable trained jointly from the answer only using reinforcement
learning. Experiments demonstrate the state of the art performance on a
challenging subset of the Wikireading and on a new dataset, while speeding up
the model by 3.5x-6.7x.
