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Active learning for rule-based and corpus-based Spoken Language Understanding models

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3 Author(s)
Gotab, P. ; LIA, Univ. d''Avignon, Avignon, France ; Bechet, F. ; Damnati, G.

Active learning can be used for the maintenance of a deployed spoken dialog system (SDS) that evolves with time and when large collection of dialog traces can be collected on a daily basis. At the spoken language understanding (SLU) level this maintenance process is crucial as a deployed SDS evolves quickly when services are added, modified or dropped. Knowledge-based approaches, based on manually written grammars or inference rules, are often preferred as system designers can modify directly the SLU models in order to take into account such a modification in the service, even if no or very little related data has been collected. However as new examples are added to the annotated corpus, corpus-based methods can then be applied, replacing or in addition to the initial knowledge-based models. This paper describes an active learning scheme, based on an SLU criterion, which is used for automatically updating the SLU models of a deployed SDS. Two kind of SLU models are going to be compared: rule-based ones, used in the deployed system and consisting of several thousands of hand-crafted rules; corpus-based ones, based on the automatic learning of classifiers on an annotated corpus.

Published in:

Automatic Speech Recognition & Understanding, 2009. ASRU 2009. IEEE Workshop on

Date of Conference:

Nov. 13 2009-Dec. 17 2009