The use of explicit user models in text generation: tailoring to a user's level of expertise

A question answering program that provides access to a large amount of data will be most useful if it can tailor its answers to each individual user. In particular, a user's level of knowledge about the domain of discourse is an important factor in this tailoring if the answer provided is to be both informative and understandable to the user. In this research, we address the issue of how the user's domain knowledge, or the level of expertise, might affect an answer. By studying texts we found that the user's level of domain knowledge affected the kind of information provided and not just the amount of information, as was previously assumed. Depending on the user's assumed domain knowledge, a description of a complex physical object can be either parts-oriented or process-oriented. Thus the user's level of expertise in a domain can guide a system in choosing the appropriate facts from the knowledge base to include in an answer. We propose two distinct descriptive strategies that can be used to generate texts aimed at naive and expert users. Users are not necessarily truly expert or fully naive however, but can be anywhere along a knowledge spectrum whose extremes are naive and expert. In this work, we show how our generation system, TAILOR, can use information about a user's level of expertise to combine several discourse strategies in a single text, choosing the most appropriate at each point in the generation process, in order to generate texts for users anywhere along the knowledge spectrum. TAILOR's ability to combine discourse strategies based on a user model allows for the generation of a wider variety of texts and the most appropriate one for the user.