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This formulation addresses a key challenge in machine comprehension by\nrequiring a standalone representation of the document discourse. It\nadditionally leads to a significant scalability advantage since the encoding of\nthe answer candidate phrases in the document can be pre-computed and indexed\noffline for efficient retrieval. We experiment with baseline models for the new\ntask, which achieve a reasonable accuracy but significantly underperform\nunconstrained QA models. We invite the QA research community to engage in\nPhrase-Indexed Question Answering (PIQA, pika) for closing the gap. 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