  The NLP community has seen substantial recent interest in grounding to
facilitate interaction between language technologies and the world. However, as
a community, we use the term broadly to reference any linking of text to data
or non-textual modality. In contrast, Cognitive Science more formally defines
"grounding" as the process of establishing what mutual information is required
for successful communication between two interlocutors -- a definition which
might implicitly capture the NLP usage but differs in intent and scope. We
investigate the gap between these definitions and seek answers to the following
questions: (1) What aspects of grounding are missing from NLP tasks? Here we
present the dimensions of coordination, purviews and constraints. (2) How is
the term "grounding" used in the current research? We study the trends in
datasets, domains, and tasks introduced in recent NLP conferences. And finally,
(3) How to advance our current definition to bridge the gap with Cognitive
Science? We present ways to both create new tasks or repurpose existing ones to
make advancements towards achieving a more complete sense of grounding.
