  To communicate with new partners in new contexts, humans rapidly form new
linguistic conventions. Recent neural language models are able to comprehend
and produce the existing conventions present in their training data, but are
not able to flexibly and interactively adapt those conventions on the fly as
humans do. We introduce an interactive repeated reference task as a benchmark
for models of adaptation in communication and propose a regularized continual
learning framework that allows an artificial agent initialized with a generic
language model to more accurately and efficiently communicate with a partner
over time. We evaluate this framework through simulations on COCO and in
real-time reference game experiments with human partners.
