Automatic generation of weather forecast texts using comprehensive probabilistic generation-space models
Anja Belz · Natural Language Engineering · 2007
Abstract Two important recent trends in natural language generation are (i) probabilistic techniques and (ii) comprehensive approaches that move away from traditional strictly modular and sequential models. This paper reports experiments in whichpcru– 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 pipelinednlgsystem and (iii) ahalogen-style statistical generator. The most striking result is that despite acquiring all decision-making abilities automatically, the bestpcrugenerators produce outputs of high enough quality to be scored more highly by human judges than forecasts written by experts.