  Natural language is ambiguous. Resolving ambiguous questions is key to
successfully answering them. Focusing on questions about images, we create a
dataset of ambiguous examples. We annotate these, grouping answers by the
underlying question they address and rephrasing the question for each group to
reduce ambiguity. Our analysis reveals a linguistically-aligned ontology of
reasons for ambiguity in visual questions. We then develop an English
question-generation model which we demonstrate via automatic and human
evaluation produces less ambiguous questions. We further show that the question
generation objective we use allows the model to integrate answer group
information without any direct supervision.
