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Charts are very popular to analyze data and convey important insights. Peopleoften analyze visualizations to answer open-ended questions that requireexplanatory answers. Answering such questions are often difficult andtime-consuming as it requires a lot of cognitive and perceptual efforts. Toaddress this challenge, we introduce a new task called OpenCQA, where the goalis to answer an open-ended question about a chart with descriptive texts. Wepresent the annotation process and an in-depth analysis of our dataset. Weimplement and evaluate a set of baselines under three practical settings. Inthe first setting, a chart and the accompanying article is provided as input tothe model. The second setting provides only the relevant paragraph(s) to thechart instead of the entire article, whereas the third setting requires themodel to generate an answer solely based on the chart. Our analysis of theresults show that the top performing models generally produce fluent andcoherent text while they struggle to perform complex logical and arithmeticreasoning.

OpenCQA: Open-ended Question Answering with Charts