A Reference Architecture for Natural Language Generation Systems

Authors:
Chris Mellish;Donia Scott;Lynne Cahill;Daniel Paiva;Roger Evans;Mike Reape
Affiliations:
Department of Computing Science, University of Aberdeen, Aberdeen, UK;Centre for Research in Computing, The Open University, Milton Keynes, UK;University of Sussex, UK;University of Sussex, UK;School of Maths and Computing, University of Brighton, Brighton, UK;School of Informatics, University of Edinburgh, Edinburgh, UK e-mail: rags@open.ac.uk
Venue:
Natural Language Engineering
Year:
2006

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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.