Paper: Generating Referential Descriptions Under Conditions Of Uncertainty

ACL ID W05-1606
Title Generating Referential Descriptions Under Conditions Of Uncertainty
Venue ENLG
Session Main Conference
Year 2005
Authors

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 differ- ently among people, due to a number of factors including vagueness, knowledge discrepancies, and limited perception capabilities. Taking these discre- pancies 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 knowl- e...