  The task of conducting visually grounded dialog involves learning
goal-oriented cooperative dialog between autonomous agents who exchange
information about a scene through several rounds of questions and answers in
natural language. We posit that requiring artificial agents to adhere to the
rules of human language, while also requiring them to maximize information
exchange through dialog is an ill-posed problem. We observe that humans do not
stray from a common language because they are social creatures who live in
communities, and have to communicate with many people everyday, so it is far
easier to stick to a common language even at the cost of some efficiency loss.
Using this as inspiration, we propose and evaluate a multi-agent
community-based dialog framework where each agent interacts with, and learns
from, multiple agents, and show that this community-enforced regularization
results in more relevant and coherent dialog (as judged by human evaluators)
without sacrificing task performance (as judged by quantitative metrics).
