  The language for expressing comparisons is often complex and nuanced, making
supporting natural language-based visual comparison a non-trivial task. To
better understand how people reason about comparisons in natural language, we
explore a design space of utterances for comparing data entities. We identified
different parameters of comparison utterances that indicate what is being
compared (i.e., data variables and attributes) as well as how these parameters
are specified (i.e., explicitly or implicitly). We conducted a user study with
sixteen data visualization experts and non-experts to investigate how they
designed visualizations for comparisons in our design space. Based on the rich
set of visualization techniques observed, we extracted key design features from
the visualizations and synthesized them into a subset of sixteen representative
visualization designs. We then conducted a follow-up study to validate user
preferences for the sixteen representative visualizations corresponding to
utterances in our design space. Findings from these studies suggest guidelines
and future directions for designing natural language interfaces and
recommendation tools to better support natural language comparisons in visual
analytics.
