Learning to Search in Long Documents Using Document Structure

Understand

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.