DVQA: Understanding Data Visualizations via Question Answering
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Bar charts are an effective way for humans to convey information to each other, but today's algorithms cannot parse them. Existing methods fail when faced with minor variations in appearance. Here, we present DVQA, a dataset that tests many aspects of bar chart understanding in a question answering framework. Unlike visual question answering (VQA), DVQA requires processing words and answers that are unique to a particular bar chart. State-of-the-art VQA algorithms perform poorly on DVQA, and we propose two strong baselines that perform considerably better. Our work will enable algorithms to automatically extract semantic information from vast quantities of literature in science, business, and other areas.
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