  A hallmark of human language is the ability to effectively and efficiently
convey contextually relevant information. One theory for how humans reason
about language is presented in the Rational Speech Acts (RSA) framework, which
captures pragmatic phenomena via a process of recursive social reasoning
(Goodman & Frank, 2016). However, RSA represents ideal reasoning in an
unconstrained setting. We explore the idea that speakers might learn to
amortize the cost of RSA computation over time by directly optimizing for
successful communication with an internal listener model. In simulations with
grounded neural speakers and listeners across two communication game datasets
representing synthetic and human-generated data, we find that our amortized
model is able to quickly generate language that is effective and concise across
a range of contexts, without the need for explicit pragmatic reasoning.
