  We improve the informativeness of models for conditional text generation
using techniques from computational pragmatics. These techniques formulate
language production as a game between speakers and listeners, in which a
speaker should generate output text that a listener can use to correctly
identify the original input that the text describes. While such approaches are
widely used in cognitive science and grounded language learning, they have
received less attention for more standard language generation tasks. We
consider two pragmatic modeling methods for text generation: one where
pragmatics is imposed by information preservation, and another where pragmatics
is imposed by explicit modeling of distractors. We find that these methods
improve the performance of strong existing systems for abstractive
summarization and generation from structured meaning representations.
