  This paper introduces a new framework for open-domain question answering in
which the retriever and the reader iteratively interact with each other. The
framework is agnostic to the architecture of the machine reading model, only
requiring access to the token-level hidden representations of the reader. The
retriever uses fast nearest neighbor search to scale to corpora containing
millions of paragraphs. A gated recurrent unit updates the query at each step
conditioned on the state of the reader and the reformulated query is used to
re-rank the paragraphs by the retriever. We conduct analysis and show that
iterative interaction helps in retrieving informative paragraphs from the
corpus. Finally, we show that our multi-step-reasoning framework brings
consistent improvement when applied to two widely used reader architectures
DrQA and BiDAF on various large open-domain datasets --- TriviaQA-unfiltered,
QuasarT, SearchQA, and SQuAD-Open.
