Automatic generation of weather forecast texts using comprehensive probabilistic generation-space models

Two important recent trends in nlg are (i) probabilistic techniques and (ii) comprehensive approaches that move away from traditional strictly modular and sequential models. This paper reports experiments in which p cru — a generation framework that combines probabilistic generation methodology with a comprehensive model of the generation space — was used to semi-automatically create five different versions of a weather forecast generator. The generators were evaluated in terms of output quality, development time and computational efficiency against (i) human forecasters, (ii) a traditional handcrafted pipelined nlg system, and (iii) a halogen-style statistical generator. The most striking result is that despite acquiring all decision-making abilities automatically, the best p cru generators produce outputs of high enough quality to be scored more highly by human judges than forecasts written by experts.