  This position paper proposes a conceptual framework for the design of Natural
Language Generation (NLG) systems that follow efficient and effective
production strategies in order to achieve complex communicative goals. In this
general framework, efficiency is characterised as the parsimonious regulation
of production and comprehension costs while effectiveness is measured with
respect to task-oriented and contextually grounded communicative goals. We
provide concrete suggestions for the estimation of goals, costs, and utility
via modern statistical methods, demonstrating applications of our framework to
the classic pragmatic task of visually grounded referential games and to
abstractive text summarisation, two popular generation tasks with real-world
applications. In sum, we advocate for the development of NLG systems that learn
to make pragmatic production decisions from experience, by reasoning about
goals, costs, and utility in a human-like way.

Computation and Language;Artificial Intelligence