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.

提出一种自然语言生成系统设计的概念框架，以实现复杂的交际目标，并通过现代统计方法提供针对目标、成本和效用的具体建议。同时，倡导开发可以通过类人的方式推理目标、成本和效用的 NLG 系统。