Abstract for Dynamically Improving Explanations: A Revision-Based Approach to Explanation Generation
15th International Joint Conference on AI, 1997

      Recent years have witnessed rapid progress in explanation generation. Despite these advances, the quality of prose produced by explanation generators warrants significant improvement. Revision-based explanation generation offers a promising means for improving explanations at runtime. In contrast to single-draft explanation generation architectures, a revision-based generator could dynamically create, evaluate, and refine multiple drafts of explanations. However, because of the inherent complexity of revision, previous multi-sentential revision-based approaches have not scaled up. We have developed a scalable revision-based model of explanation generation that dynamically improves multi-sentential explanations. By operating on abstract discourse plans encoded in a minimalist representation, it combats both the conceptual complexities and the efficiency problems posed by revision. This approach has been implemented in Revisor, a unification-based revision system. Evaluations of Revisor's performance in generating a corpus of extended multi-sentential scientific explanations yielded encouraging results.




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