Verbal communication is an ubiquitous aspect of human interaction occurring in many contexts; however, it is primarily studied in the limited context of two people communicating information. Understanding communication in complex, multi-party interactions is both a scientific challenge for psycholinguistics and an engineering challenge for creating artificial agents who can participate in these richer contexts. We adapted the reference game paradigm to an online 3-player game where players refer to objects in order to coordinate selections based on the available utilities. We ran games with shared or individual payoffs and with or without access to language. Our paradigm can also be used for artificial agents; we trained reinforcement learning-based agents on the same task as a comparison. Our dataset shows the same patterns found in simpler reference games and contains rich language of reference and negotiation.