  In Emergent Communication (EC) agents learn to communicate with one another,
but the protocols that they develop are specialised to their training
community. This observation led to research into Zero-Shot Coordination (ZSC)
for learning communication strategies that are robust to agents not encountered
during training. However, ZSC typically assumes that no prior data is available
about the agents that will be encountered in the zero-shot setting. In many
cases, this presents an unnecessarily hard problem and rules out communication
via preestablished conventions. We propose a novel AI challenge called a
Cooperative Language Acquisition Problem (CLAP) in which the ZSC assumptions
are relaxed by allowing a 'joiner' agent to learn from a dataset of
interactions between agents in a target community. We propose and compare two
methods for solving CLAPs: Imitation Learning (IL), and Emergent Communication
pretraining and Translation Learning (ECTL), in which an agent is trained in
self-play with EC and then learns from the data to translate between the
emergent protocol and the target community's protocol.
