  Human communication relies on common ground (CG), the mutual knowledge and
beliefs shared by participants, to produce coherent and interesting
conversations. In this paper, we demonstrate that current response generation
(RG) models produce generic and dull responses in dialogues because they act
reflexively, failing to explicitly model CG, both due to the lack of CG in
training data and the standard RG training procedure. We introduce Reflect, a
dataset that annotates dialogues with explicit CG (materialized as inferences
approximating shared knowledge and beliefs) and solicits 9k diverse
human-generated responses each following one common ground. Using Reflect, we
showcase the limitations of current dialogue data and RG models: less than half
of the responses in current data are rated as high quality (sensible, specific,
and interesting) and models trained using this data have even lower quality,
while most Reflect responses are judged high quality. Next, we analyze whether
CG can help models produce better-quality responses by using Reflect CG to
guide RG models. Surprisingly, we find that simply prompting GPT3 to "think"
about CG generates 30% more quality responses, showing promising benefits to
integrating CG into the RG process.
