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.

本研究提出了一种基于 Pragmatic Rational Speaker 的框架，通过在工作记忆中添加轻量级差异调整层来学习演讲者 - 听者差异并相应地调整演讲，从而实现了一种类似于人类交流的 AI 代理能力。实证结果证明，该框架能够显著提高协作任务的结果。