Towards Reducing and Managing Uncertainty in Spoken Dialogue Systems
Raquel Fernández, Andrea Corradini, David Schlangen, Manfred Stede · Publikationen an der Universität Bielefeld (Universität Bielefeld) · 2007
Spoken dialogue systems (sdss)—computational systems that can engage in a dialogue with a human user about a restricted topic by understanding and reacting to spoken natural language—have a valuable potential not only as commercial systems that can handle useful tasks in real situations, but also as a test bed for semantic and pragmatic theories of dialogue interaction. Besides the difficulties inherent to any natural language understanding system (like e.g. ambiguity and context-dependence), sdss are faced with additional challenges that often derive from the lack of technical accuracy of their components, most prominently the automatic speech recogniser (ASR). For this reason, sdss are confronted with a great degree of uncertainty when processing user utterances. In those cases where understanding does not fail completely, a system will typically be able to form some hypotheses about the input received from the user. However, judging the quality of these hypotheses is itself a highly uncertain task, and finding answers to questions such as ‘Did the user really say X?’, ‘Did the user mean Y?’ or ‘Is Z what the user intended me to do?’ can be a very hard enterprise for a dialogue system. One of the crucial aspects that contributes to reducing uncertainty is the use of meaningful clarification and grounding strategies that are able to tackle the problems that the system encounters and, when appropriate, give feedback to the user about its internal representations. In this paper we describe ongoing work carried out within the project “DEAWU: DEAling With Uncertainty in Spoken Dialogue Systems”.1 The