1 research outputs found
Continual adaptation for efficient machine communication
To communicate with new partners in new contexts, humans rapidly form new
linguistic conventions. Recent language models trained with deep neural
networks 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 a 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