  Human communication is a collaborative process. Speakers, on top of conveying
their own intent, adjust the content and language expressions by taking the
listeners into account, including their knowledge background, personalities,
and physical capabilities. Towards building AI agents with similar abilities in
language communication, we propose Pragmatic Rational Speaker (PRS), a
framework extending Rational Speech Act (RSA). The PRS attempts to learn the
speaker-listener disparity and adjust the speech accordingly, by adding a
light-weighted disparity adjustment layer into working memory on top of
speaker's long-term memory system. By fixing the long-term memory, the PRS only
needs to update its working memory to learn and adapt to different types of
listeners. To validate our framework, we create a dataset that simulates
different types of speaker-listener disparities in the context of referential
games. Our empirical results demonstrate that the PRS is able to shift its
output towards the language that listener are able to understand, significantly
improve the collaborative task outcome.
