Compositional properties of emergent languages in deep learning

Usually two or more agents play a cooperative game where agents' goals are common but they access different information. In order to solve the given task agents have to share useful information with each other through a discrete bottleneck called the communication channel. The discrete symbols in the message do not have any a priori meaning but agents learn to cooperate by attributing meaning to the messages; a language protocol emerges as a byproduct of the training process. This emergent language serves only one goal: to complete the task successfully. One of the promises of this approach is to provide meaningful insights into the early stages of human language emergence as a result of cooperation.