  Reading comprehension models are based on recurrent neural networks that
sequentially process the document tokens. As interest turns to answering more
complex questions over longer documents, sequential reading of large portions
of text becomes a substantial bottleneck. Inspired by how humans use document
structure, we propose a novel framework for reading comprehension. We represent
documents as trees, and model an agent that learns to interleave quick
navigation through the document tree with more expensive answer extraction. To
encourage exploration of the document tree, we propose a new algorithm, based
on Deep Q-Network (DQN), which strategically samples tree nodes at training
time. Empirically we find our algorithm improves question answering performance
compared to DQN and a strong information-retrieval (IR) baseline, and that
ensembling our model with the IR baseline results in further gains in
performance.
