  People rely heavily on context to enrich meaning beyond what is literally
said, enabling concise but effective communication. To interact successfully
and naturally with people, user-facing artificial intelligence systems will
require similar skills in pragmatics: relying on various types of context --
from shared linguistic goals and conventions, to the visual and embodied world
-- to use language effectively. We survey existing grounded settings and
pragmatic modeling approaches and analyze how the task goals, environmental
contexts, and communicative affordances in each work enrich linguistic meaning.
We present recommendations for future grounded task design to naturally elicit
pragmatic phenomena, and suggest directions that focus on a broader range of
communicative contexts and affordances.
