Flexible Generation: Taking the User into Account

Abstract

Sophisticated computer systems capable of interacting with people using natural language are becoming increasingly common. These systems need to interact with a wide variety of users in different situations. Typically, these systems have access to large amounts of data and must select from these data the information to present to the user. No single generated text will be adequate across all user types and all situations. Certainly, people plan what they will say or write based in part on their knowledge of the listener or intended reader. Similarly, computer systems that produce language must take their listeners/readers into account in order to be effective. In particular, the user’s level of knowledge about the domain of discourse is an important factor in this tailoring, if the text provided is to be both informative and understandable to the user. The text should not contain information that is already known or can be easily inferred, nor should it include facts that the user cannot understand. This paper demonstrates the feasibility of incorporating the user’s domain knowledge or user’s expertise, into a text generation system and addresses the issue of how this factor might affect the content, organization and phrasing of a text. We look at two applications domains: (i) generating descriptions of complex physical objects, and (ii) generating documentation for programming languages. We show how a computer generation system can make use of both stereotypical and individualized user models.