Dynamically Imroving Explanations: A Revision-Based Approach to Explanation Generation

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 singledraft 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 multisentential 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.