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Recent work on open domain question answering (QA) assumes strong supervisionof the supporting evidence and/or assumes a blackbox information retrieval (IR)system to retrieve evidence candidates. We argue that both are suboptimal,since gold evidence is not always available, and QA is fundamentally differentfrom IR. We show for the first time that it is possible to jointly learn theretriever and reader from question-answer string pairs and without any IRsystem. In this setting, evidence retrieval from all of Wikipedia is treated asa latent variable. Since this is impractical to learn from scratch, wepre-train the retriever with an Inverse Cloze Task. We evaluate on openversions of five QA datasets. On datasets where the questioner already knowsthe answer, a traditional IR system such as BM25 is sufficient. On datasetswhere a user is genuinely seeking an answer, we show that learned retrieval iscrucial, outperforming BM25 by up to 19 points in exact match.

Latent Retrieval for Weakly Supervised Open Domain Question Answering