This paper describes a probabilistic mechanism for the interpretation of sentence sequences developed for a spoken dialogue system mounted on a robotic agent. The mechanism receives as input a sequence of sentences, and produces an interpretation which integrates the interpretations of individual sentences. For our evaluation, we collected a corpus of hypothetical requests to a robot. Our mechanism exhibits good performance for sentence pairs, but requires further improvements for sentence sequences.
We describe a probabilistic reference disambiguation mechanism developed for a spoken dialogue system mounted on an autonomous robotic agent. Our mechanism processes referring expressions containing intrinsic features of objects (lexical item, colour and size) and locative expressions, which involve more than one concept. The intended objects are identified in the context of the output of a simulated scene analysis system, which returns the colour and size of the seen objects and a distribution for their type. The evaluation of our system shows high resolution performance across a range of spoken referring expressions and simulated vision accuracies.
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