  $\textit{No man is an island.}$ Humans communicate with a large community by
coordinating with different interlocutors within short conversations. This
ability has been understudied by the research on building neural communicative
agents. We study the task of few-shot $\textit{language coordination}$: agents
quickly adapting to their conversational partners' language abilities.
Different from current communicative agents trained with self-play, we require
the lead agent to coordinate with a $\textit{population}$ of agents with
different linguistic abilities, quickly adapting to communicate with unseen
agents in the population. This requires the ability to model the partner's
beliefs, a vital component of human communication. Drawing inspiration from
theory-of-mind (ToM; Premack& Woodruff (1978)), we study the effect of the
speaker explicitly modeling the listeners' mental states. The speakers, as
shown in our experiments, acquire the ability to predict the reactions of their
partner, which helps it generate instructions that concisely express its
communicative goal. We examine our hypothesis that the instructions generated
with ToM modeling yield better communication performance in both a referential
game and a language navigation task. Positive results from our experiments hint
at the importance of explicitly modeling communication as a socio-pragmatic
progress.
