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Visual question answering (VQA) is a task of answering a visual question thatis a pair of question and image. Some visual questions are ambiguous and someare clear, and it may be appropriate to change the ambiguity of questions fromsituation to situation. However, this issue has not been addressed by any priorwork. We propose a novel task, rephrasing the questions by controlling theambiguity of the questions. The ambiguity of a visual question is defined bythe use of the entropy of the answer distribution predicted by a VQA model. Theproposed model rephrases a source question given with an image so that therephrased question has the ambiguity (or entropy) specified by users. Wepropose two learning strategies to train the proposed model with the VQA v2dataset, which has no ambiguity information. We demonstrate the advantage ofour approach that can control the ambiguity of the rephrased questions, and aninteresting observation that it is harder to increase than to reduce ambiguity.

Rephrasing visual questions by specifying the entropy of the answer distribution