  Natural language interfaces (NLIs) have become a prevalent medium for
conducting visual data analysis, enabling people with varying levels of
analytic experience to ask questions of and interact with their data. While
there have been notable improvements with respect to language understanding
capabilities in these systems, fundamental user experience and interaction
challenges including the lack of analytic guidance (i.e., knowing what aspects
of the data to consider) and discoverability of natural language input (i.e.,
knowing how to phrase input utterances) persist. To address these challenges,
we investigate utterance recommendations that contextually provide analytic
guidance by suggesting data features (e.g., attributes, values, trends) while
implicitly making users aware of the types of phrasings that an NLI supports.
We present SNOWY, a prototype system that generates and recommends utterances
for visual analysis based on a combination of data interestingness metrics and
language pragmatics. Through a preliminary user study, we found that utterance
recommendations in SNOWY support conversational visual analysis by guiding the
participants' analytic workflows and making them aware of the system's language
interpretation capabilities. Based on the feedback and observations from the
study, we discuss potential implications and considerations for incorporating
recommendations in future NLIs for visual analysis.
