Incremental reference resolution: the task, metrics for evaluation, and a Bayesian filtering model that is sensitive to disfluencies

Authors:
David Schlangen;Timo Baumann;Michaela Atterer
Affiliations:
University of Potsdam, Germany;University of Potsdam, Germany;University of Potsdam, Germany
Venue:
SIGDIAL '09 Proceedings of the SIGDIAL 2009 Conference: The 10th Annual Meeting of the Special Interest Group on Discourse and Dialogue
Year:
2009

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Abstract

In this paper we do two things: a) we discuss in general terms the task of incremental reference resolution (IRR), in particular resolution of exophoric reference, and specify metrics for measuring the performance of dialogue system components tackling this task, and b) we present a simple Bayesian filtering model of IRR that performs reasonably well just using words directly (no structure information and no hand-coded semantics): it picks the right referent out of 12 for around 50% of real-world dialogue utterances in our test corpus. It is also able to learn to interpret not only words but also hesitations, just as humans have shown to do in similar situations, namely as markers of references to hard-to-describe entities.