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Natural language is ambiguous. Resolving ambiguous questions is key tosuccessfully answering them. Focusing on questions about images, we create adataset of ambiguous examples. We annotate these, grouping answers by theunderlying question they address and rephrasing the question for each group toreduce ambiguity. Our analysis reveals a linguistically-aligned ontology ofreasons for ambiguity in visual questions. We then develop an Englishquestion-generation model which we demonstrate via automatic and humanevaluation produces less ambiguous questions. We further show that the questiongeneration objective we use allows the model to integrate answer groupinformation without any direct supervision.

Why Did the Chicken Cross the Road? Rephrasing and Analyzing Ambiguous Questions in VQA