ChartQA: A Benchmark for Question Answering about Charts with Visual and Logical Reasoning
Ahmed Masry, Xuan Long, Jia Qing Tan, Shafiq Joty, Enamul Hoque · Findings of the Association for Computational Linguistics: ACL 2022 · 2022
Charts are very popular for analyzing data.When exploring charts, people often ask a variety of complex reasoning questions that involve several logical and arithmetic operations.They also commonly refer to visual features of a chart in their questions.However, most existing datasets do not focus on such complex reasoning questions as their questions are template-based and answers come from a fixedvocabulary.In this work, we present a largescale benchmark covering 9.6K human-written questions as well as 23.1K questions generated from human-written chart summaries.To address the unique challenges in our benchmark involving visual and logical reasoning over charts, we present two transformer-based models that combine visual features and the data table of the chart in a unified way to answer questions.While our models achieve the state-of-the-art results on the previous datasets as well as on our benchmark, the evaluation also reveals several challenges in answering complex reasoning questions.