  Reading Comprehension has received significant attention in recent years as
high quality Question Answering (QA) datasets have become available. Despite
state-of-the-art methods achieving strong overall accuracy, Multi-Hop (MH)
reasoning remains particularly challenging. To address MH-QA specifically, we
propose a Deep Reinforcement Learning based method capable of learning
sequential reasoning across large collections of documents so as to pass a
query-aware, fixed-size context subset to existing models for answer
extraction. Our method is comprised of two stages: a linker, which decomposes
the provided support documents into a graph of sentences, and an extractor,
which learns where to look based on the current question and already-visited
sentences. The result of the linker is a novel graph structure at the sentence
level that preserves logical flow while still allowing rapid movement between
documents. Importantly, we demonstrate that the sparsity of the resultant graph
is invariant to context size. This translates to fewer decisions required from
the Deep-RL trained extractor, allowing the system to scale effectively to
large collections of documents.
  The importance of sequential decision making in the document traversal step
is demonstrated by comparison to standard IE methods, and we additionally
introduce a BM25-based IR baseline that retrieves documents relevant to the
query only. We examine the integration of our method with existing models on
the recently proposed QAngaroo benchmark and achieve consistent increases in
accuracy across the board, as well as a 2-3x reduction in training time.
