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—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.(More)
—We analyze the problem of call-type classification using data that is weakly labelled. The training data is not systematically annotated, but we consider we have a weak or lazy oracle able to answer the question " Is sample x of class q? " by a simple 'yes' or 'no' answer. This situation of learning might be encountered in many real-world problems where(More)
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