Phrasing a Text in Terms the User Can Understand

Abstract

When humans use language, they show an essential, inbuilt responsiveness to their hearers/readers. When language is generated by machine, it is similarly necessary to ensure that that language is appropriate for its intended audience. Much of previous research on text generation and user modelling has focused on building a user model and selecting appropriate information from the knowledge base to present to the user. It is important, however, that the phrasing of a text be also tailored to the hearer - otherwise it may be just as ineffective as texts which wrongly direct attention or which rely on knowledge that the hearer does not have. This research proposes a new mechanism which allows the text planning process to specifically tailor syntactic phrasing to the hearer type . This is done in the context of an expert system explanation facility that needs to produce explanations of the expert system's behavior for a variety of different users - users who differ in goals, expectations, and expertise concerning both the expert system and its domain.