Exploiting user feedback to compensate for the unreliability of user models

Johanna D. Moore*, Cécile L. Paris

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

26 Citations (Scopus)


Natural Language is a powerful medium for interacting with users, and sophisticated computer systems using natural language are becoming more prevalent. Just as human speakers show an essential, inbuilt responsiveness to their hearers, computer systems must "tailor" their utterances to users. Recognizing this, researchers devised user models and strategies for exploiting them in order to enable systems to produce the "best" answer for a particular user. Because these efforts were largely devoted to investigating how a user model could be exploited to produce better responses, systems employing them typically assumed that a detailed and correct model of the user was available a priori, and that the information needed to generate appropriate responses was included in that model. However, in practice, the completeness and accuracy of a user model cannot be guaranteed. Thus, unless systems can compensate for incorrect or incomplete user models, the impracticality of building user models will prevent much of the work on tailoring from being successfully applied in real systems. In this paper, we argue that one way for a system to compensate for an unreliable user model is to be able to react to feedback from users about the suitability of the texts it produces. We also discuss how such a capability can actually alleviate some of the burden now placed on user modeling. Finally, we present a text generation system that employs whatever information is available in its user model in an attempt to produce satisfactory texts, but is also capable of responding to the user's follow-up questions about the texts it produces.

Original languageEnglish
Pages (from-to)287-330
Number of pages44
JournalUser Modeling and User-Adapted Interaction
Issue number4
Publication statusPublished - Dec 1992


  • adaptive systems
  • expert systems
  • explanation
  • natural language generation
  • question answering
  • text planning
  • user modeling


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