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Reading comprehension models are based on recurrent neural networks thatsequentially process the document tokens. As interest turns to answering morecomplex questions over longer documents, sequential reading of large portionsof text becomes a substantial bottleneck. Inspired by how humans use documentstructure, we propose a novel framework for reading comprehension. We representdocuments as trees, and model an agent that learns to interleave quicknavigation through the document tree with more expensive answer extraction. Toencourage exploration of the document tree, we propose a new algorithm, basedon Deep Q-Network (DQN), which strategically samples tree nodes at trainingtime. Empirically we find our algorithm improves question answering performancecompared to DQN and a strong information-retrieval (IR) baseline, and thatensembling our model with the IR baseline results in further gains inperformance.

Learning to Search in Long Documents Using Document Structure