Pierre Gotab

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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)
Deployed Spoken Dialog Systems (SDS) evolve quickly while new services are added or dropped, and while users’ behaviour change. This dynamic aspect of SDS justifies the need for a process allowing the system to keep up to date the Automatic Speech Recognition (ASR) and the Spoken Language Understanding (SLU) models. This process usually consists in(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 the(More)
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