  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 fixed-vocabulary. In this work, we present a large-scale 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.
