Consulting a user model to address a user's inferences during content planning

Ingrid Zukerman, Richard Mcconachy

    Research output: Contribution to journalArticleResearchpeer-review

    4 Citations (Scopus)

    Abstract

    Most Natural Language Generation systems developed to date assume that a user will learn only what is explicitly stated in the discourse. This assumption leads to the generation of discourse that states explicitly all the information to be conveyed, and does not address further inferences from the discourse. In this paper, we describe a student model which provides a qualitative representation of a student's beliefs and inferences, and a content planning mechanism which consults this model in order to address the above problems. Our mechanism performs inferences in backward reasoning mode to generate discourse that conveys the intended information, and in forward reasoning mode to draw conclusions from the presented information. The forward inferences enable our mechanism to address possible incorrect inferences from the discourse, and to omit information that may be easily inferred from the discourse. In addition, our mechanism improves the conciseness of the generated discourse by omitting information known by the student. The domain of our implementation is the explanation of concepts in high school algebra.

    Original languageEnglish
    Pages (from-to)155-185
    Number of pages31
    JournalUser Modeling and User-Adapted Interaction
    Volume3
    Issue number2
    DOIs
    Publication statusPublished - 1 Jun 1993

    Keywords

    • backward reasoning
    • content planning
    • forward reasoning
    • inferences
    • student beliefs

    Cite this

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    abstract = "Most Natural Language Generation systems developed to date assume that a user will learn only what is explicitly stated in the discourse. This assumption leads to the generation of discourse that states explicitly all the information to be conveyed, and does not address further inferences from the discourse. In this paper, we describe a student model which provides a qualitative representation of a student's beliefs and inferences, and a content planning mechanism which consults this model in order to address the above problems. Our mechanism performs inferences in backward reasoning mode to generate discourse that conveys the intended information, and in forward reasoning mode to draw conclusions from the presented information. The forward inferences enable our mechanism to address possible incorrect inferences from the discourse, and to omit information that may be easily inferred from the discourse. In addition, our mechanism improves the conciseness of the generated discourse by omitting information known by the student. The domain of our implementation is the explanation of concepts in high school algebra.",
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    Consulting a user model to address a user's inferences during content planning. / Zukerman, Ingrid; Mcconachy, Richard.

    In: User Modeling and User-Adapted Interaction, Vol. 3, No. 2, 01.06.1993, p. 155-185.

    Research output: Contribution to journalArticleResearchpeer-review

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    T1 - Consulting a user model to address a user's inferences during content planning

    AU - Zukerman, Ingrid

    AU - Mcconachy, Richard

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    N2 - Most Natural Language Generation systems developed to date assume that a user will learn only what is explicitly stated in the discourse. This assumption leads to the generation of discourse that states explicitly all the information to be conveyed, and does not address further inferences from the discourse. In this paper, we describe a student model which provides a qualitative representation of a student's beliefs and inferences, and a content planning mechanism which consults this model in order to address the above problems. Our mechanism performs inferences in backward reasoning mode to generate discourse that conveys the intended information, and in forward reasoning mode to draw conclusions from the presented information. The forward inferences enable our mechanism to address possible incorrect inferences from the discourse, and to omit information that may be easily inferred from the discourse. In addition, our mechanism improves the conciseness of the generated discourse by omitting information known by the student. The domain of our implementation is the explanation of concepts in high school algebra.

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    KW - forward reasoning

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