Participatiing in Explanatory Dialogues

While much has been written about the areas of text generation, text planning, discourse modeling, and user modeling, Johanna Moore's book is one of the first to tackle modeling the complex dynamics of explanatory dialogues. It describes an explanation-planning architecture that enables a computational system to participate in an interactive dialogue with its users, focusing on the knowledge structures that a system must build in order to elaborate or clarify prior utterances, or to...
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While much has been written about the areas of text generation, text planning, discourse modeling, and user modeling, Johanna Moore's book is one of the first to tackle modeling the complex dynamics of explanatory dialogues. It describes an explanation-planning architecture that enables a computational system to participate in an interactive dialogue with its users, focusing on the knowledge structures that a system must build in order to elaborate or clarify prior utterances, or to answer follow-up questions in the context of an ongoing dialogue.

Moore develops a model of explanation generation and describes a fully implemented natural-language system that is embedded in an existing expert system and that includes a generation component. Her main thesis is that shallow approaches to explanation such as paraphrasing the expert system's line of reasoning or filling in an explanation "schema" are not adequate for supporting dialogue, and that a more flexible approach is needed, one that is adaptive to context, aware of what is being said, and of what has gone before in the user's dialogue with the expert system. She argues that the problem with prior approaches is that they do not provide a representation of the intended effects of the components of an explanation, nor how these intentions are related to one another or to the rhetorical structure of the text. She proposes a computational solution to the question of how explanations can be synthesized in such a way that a system can later reason about the explanations it has produced to affect its subsequent utterances.

ACL-MIT Series in Natural Language Processing