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

Published online by Cambridge University Press:  01 October 2008

ANJA BELZ
ANJA BELZ*
Affiliation:
Natural Language Technology Group, School of Computing, Mathematical and Information Sciences, University of Brighton, Lewes Road, Brighton BN2 4GJ, UK.
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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 which pcru – 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 pcru generators produce outputs of high enough quality to be scored more highly by human judges than forecasts written by experts.

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Type
Papers
Information
Natural Language Engineering , Volume 14 , Issue 4 , October 2008 , pp. 431 - 455
Copyright
Copyright © Cambridge University Press 2007

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