A Reference Architecture for Generation Systems

  • Authors:
  • Chris Mellish;Mike Reape;Donia Scott;Lynne Cahill;Roger Evans;Daniel Paiva

  • Affiliations:
  • School of Informatics, University of Edinburgh, Appleton Tower, Crichton St, Edinburgh, UK e-mail: cmellish@csd.abdn.ac.uk;School of Informatics, University of Edinburgh, Appleton Tower, Crichton St, Edinburgh, UK e-mail: cmellish@csd.abdn.ac.uk;Information Technology Research Institute, University of Brighton, Lewes Rd, Brighton, UK e-mail: http://www.itri.brighton.ac.uk/rags;Information Technology Research Institute, University of Brighton, Lewes Rd, Brighton, UK e-mail: http://www.itri.brighton.ac.uk/rags;Information Technology Research Institute, University of Brighton, Lewes Rd, Brighton, UK e-mail: http://www.itri.brighton.ac.uk/rags;Information Technology Research Institute, University of Brighton, Lewes Rd, Brighton, UK e-mail: http://www.itri.brighton.ac.uk/rags

  • Venue:
  • Natural Language Engineering
  • Year:
  • 2004

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Abstract

We present the RAGS (Reference Architecture for Generation Systems) framework, a specification of an abstract Natural Language Generation (NLG) system architecture to support sharing, re-use, comparison and evaluation of NLG technologies. We argue that the evidence from a survey of actual NLG systems calls for a different emphasis in a reference proposal from that seen in similar initiatives in information extraction and multimedia interfaces. We introduce the framework itself, in particular the two-level data model that allows us to support the complex data requirements of NLG systems in a flexible and coherent fashion, and describe our efforts to validate the framework through a range of implementations.