Distributional models and other supervised models of language focus on the structure of language and are an excellent way to learn general statistical associations between sequences of symbols. However, they do not capture the functional aspects of communication, i.e., that humans have intentions and use words to coordinate with others and make things happen in the real world. In this talk, I will present two studies on multi-agent emergent communication, where agents exist in some grounded environment and have to communicate about objects and their properties. This process requires the negotiation of linguistic meaning in this pragmatic context of achieving their goal. In the first study, I will present experiments in which agents learn to form a common ground that allow them to communicate about disentangled (i.e., feature norm) and entangled (i.e., raw pixels) input. In the second study, I will talk about properties of linguistic communication as arising in the context of self-interested agents.