Integrating natural language generation and model-based reasoning for explanation generation

Donald Matheson*, George M. Coghill, Somayujulu Sripada

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Natural Language Generation (NLG) systems can utilise external reasoning to explain events within a given domain. Most often, these explanations are derived from compiled knowledge such as that found in expert systems, which can lead to gaps in the explanation system's ability to justify the outcome of the reasoner. We propose the use of a deeper knowledge source for explanation generation and show how to utilise a model-based approach that uses first-principles knowledge of the problem domain to generate and justify more in-depth explanations of events in the domain. Our integrated framework of NLG and model-based reasoning makes two contributions: the articulation of model-based reasoning and justification in natural language, and the natural language generation engine exploiting the deeper knowledge available in the underlying model.

Original languageEnglish
Title of host publication2012 12th UK Workshop on Computational Intelligence, UKCI 2012
Number of pages7
Publication statusPublished - 5 Dec 2012
Event2012 12th UK Workshop on Computational Intelligence, UKCI 2012 - Edinburgh, United Kingdom
Duration: 5 Sep 20127 Sep 2012


Conference2012 12th UK Workshop on Computational Intelligence, UKCI 2012
CountryUnited Kingdom


ASJC Scopus subject areas

  • Artificial Intelligence
  • Computational Theory and Mathematics

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