Reasoning About Pragmatics with Neural Listeners and Speakers

Understand

We present a model for pragmatically describing scenes, in which contrastive behavior results from a combination of inference-driven pragmatics and learned semantics.

• Like previous learned approaches to language generation, our model uses a simple feature-driven architecture (here a pair of neural "listener" and "speaker" models) to ground language in the world.
• Like inference-driven approaches to pragmatics, our model actively reasons about listener behavior when selecting utterances.
• For training, our approach requires only ordinary captions, annotated _without_ demonstration of the pragmatic behavior the model ultimately exhibits.