Speakers use different language to communicate with partners in different communities. But how do we learn and represent which conventions to use with which partners? In this paper, we argue that solving this challenging computational problem requires speakers to supplement their lexical representations with knowledge of social group structure. We formalize this idea by extending a recent hierarchical Bayesian model of convention formation with an intermediate layer explicitly representing the latent communities each partner belongs to, and derive predictions about how conventions formed within a group ought to extend to new in-group and out-group members. We then present evidence from two behavioral experiments testing these predictions using a minimal group paradigm. Taken together, our findings provide a first step toward a formal framework for understanding the interplay between language use and social group knowledge.