Combining Discourse Strategies to Generate Descriptions to Users Along a Naive/Expert Spectrum

Abstract

A question answerin$ system that provides access to a large amount of data will be most useful if it can tailor its answer to each user. In particular, a user's level of knowledge about the domain of discourse is an important factor in this tailoring. In previous work we determined that a user's level of domain knowledge affects the kind of information provided in an answer to a user's question as opposed to just the amount of information, as was previously proposed. We also explained how two distinct discourse strategies could be used to generate texts aimed at naive and expert users. Users are not necessarily truly expert or fully na1ve however, but can be anywhere along a knowledge spectrum whose extremes are nlllve 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.