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Ambiguity is inherent to open-domain question answering; especially whenexploring new topics, it can be difficult to ask questions that have a single,unambiguous answer. In this paper, we introduce AmbigQA, a new open-domainquestion answering task which involves finding every plausible answer, and thenrewriting the question for each one to resolve the ambiguity. To study thistask, we construct AmbigNQ, a dataset covering 14,042 questions from NQ-open,an existing open-domain QA benchmark. We find that over half of the questionsin NQ-open are ambiguous, with diverse sources of ambiguity such as event andentity references. We also present strong baseline models for AmbigQA which weshow benefit from weakly supervised learning that incorporates NQ-open,strongly suggesting our new task and data will support significant futureresearch effort. Our data and baselines are available athttps://nlp.cs.washington.edu/ambigqa.

AmbigQA: Answering Ambiguous Open-domain Questions