  Charts are very popular for analyzing data, visualizing key insights and
answering complex reasoning questions about data. To facilitate chart-based
data analysis using natural language, several downstream tasks have been
introduced recently such as chart question answering and chart summarization.
However, most of the methods that solve these tasks use pretraining on language
or vision-language tasks that do not attempt to explicitly model the structure
of the charts (e.g., how data is visually encoded and how chart elements are
related to each other). To address this, we first build a large corpus of
charts covering a wide variety of topics and visual styles. We then present
UniChart, a pretrained model for chart comprehension and reasoning. UniChart
encodes the relevant text, data, and visual elements of charts and then uses a
chart-grounded text decoder to generate the expected output in natural
language. We propose several chart-specific pretraining tasks that include: (i)
low-level tasks to extract the visual elements (e.g., bars, lines) and data
from charts, and (ii) high-level tasks to acquire chart understanding and
reasoning skills. We find that pretraining the model on a large corpus with
chart-specific low- and high-level tasks followed by finetuning on three
down-streaming tasks results in state-of-the-art performance on three
downstream tasks.
