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

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.