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Why Did the Chicken Cross the Road? Rephrasing and Analyzing Ambiguous Questions in VQA
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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.
Forward citations
Cited by 2 Pith papers
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Acknowledging Focus Ambiguity in Visual Questions
VQ-FocusAmbiguity is a 5,500-example dataset annotating all plausible focus regions for visual questions, and state-of-the-art models perform poorly at recognizing and locating focus ambiguity.
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Visual question answering: from early developments to recent advances -- a survey
A survey that classifies VQA architectures by encoder, fusion, and decoder, reviews datasets and metrics, and discusses applications and future directions.
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