Learning to refer informatively by amortizing pragmatic reasoning
Abstract
A hallmark of human language is the ability to effectively andefficiently convey contextually relevant information. One the-ory for how humans reason about language is presented in theRational Speech Acts (RSA) framework, which captures prag-matic phenomena via a process of recursive social reasoning(Goodman & Frank, 2016). However, RSA represents idealreasoning in an unconstrained setting. We explore the idea thatspeakers might learn to amortize the cost of RSA computationover time by directly optimizing for successful communicationwith an internal listener model. In simulations with groundedneural speakers and listeners across two communication gamedatasets representing synthetic and human-generated data, wefind that our amortized model is able to quickly generate lan-guage that is effective and concise across a range of contexts,without the need for explicit pragmatic reasoning.