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Communication Dans Un Congrès Année : 2009

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

Pierre Gotab
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Frederic Bechet #2

Résumé

—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.
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Dates et versions

hal-01317421 , version 1 (18-05-2016)

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Citer

Pierre Gotab, Frederic Bechet #2, Geraldine Damnati. Active learning for rule-based and corpus-based Spoken Language Understanding models. IEEE Workshop on Automatic Speech Recognition & Understanding, 2009. ASRU 2009, Dec 2009, Merano, Italy. ⟨10.1109/ASRU.2009.5373377⟩. ⟨hal-01317421⟩

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