A Probabilistic Model for Understanding Composite Spoken Descriptions

Authors:
Enes Makalic;Ingrid Zukerman;Michael Niemann;Daniel Schmidt
Affiliations:
Faculty of Information Technology, Monash University, Clayton, Australia 3800;Faculty of Information Technology, Monash University, Clayton, Australia 3800;Faculty of Information Technology, Monash University, Clayton, Australia 3800;Faculty of Information Technology, Monash University, Clayton, Australia 3800
Venue:
PRICAI '08 Proceedings of the 10th Pacific Rim International Conference on Artificial Intelligence: Trends in Artificial Intelligence
Year:
2008

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Abstract

We describe a probabilistic reference disambiguation mechanism developed for a spoken dialogue system mounted on an autonomous robotic agent. Our mechanism receives as input referring expressions containing intrinsic features of individual concepts (lexical item, size and colour) and features involving more than one concept (ownership and location). It then performs probabilistic comparisons between the given features and features of objects in the domain, yielding a ranked list of candidate referents. Our evaluation shows high reference resolution accuracy across a range of spoken referring expressions.