Generating Referential Descriptions Under Conditions of Uncertainty
نویسنده
چکیده
Algorithms for generating referring expressions typically assume that an object in a scenary can be identified through a set of commonly agreed properties. This is a strong assumption, since in reality properties of objects may be perceived differently among people, due to a number of factors including vagueness, knowledge discrepancies, and limited perception capabilities. Taking these discrepancies into account, we reinterpret concepts of algorithms generating referring expressions in view of uncertainties about the appearance of objects. Our model includes two complementary measures of likelihood in object identification, and adapted property selection and termination criteria. The approach is relevant for situations with potential perception problems and for scenarios with knowledge discrepancies between conversants.
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