  We formalize a new modular variant of current question answering tasks by
enforcing complete independence of the document encoder from the question
encoder. This formulation addresses a key challenge in machine comprehension by
requiring a standalone representation of the document discourse. It
additionally leads to a significant scalability advantage since the encoding of
the answer candidate phrases in the document can be pre-computed and indexed
offline for efficient retrieval. We experiment with baseline models for the new
task, which achieve a reasonable accuracy but significantly underperform
unconstrained QA models. We invite the QA research community to engage in
Phrase-Indexed Question Answering (PIQA, pika) for closing the gap. The
leaderboard is at: nlp.cs.washington.edu/piqa
