Deployed Spoken Dialog Systems (SDS) evolve quickly while new services are added or dropped, and while users' behavior change. This dynamic aspect of SDS justify 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 in order to take into account this variability. This process usually consists in collecting new data from the deployed system, trancribing and annotating them, then adding these new examples to the ASR and SLU training corpora in order to retrain the models. This strategy, even when used with an active learning scheme, is costly as the transcription and annotation processes of the new collected samples has to be done manually. Because of this cost the models can't be adapted on a daily bases and the SDS remain unchanged between two revisions. This paper proposes a supervised approach for updating the SLU models of a deployed SDS which doesn't need any additional manual transcription or annotation processes. The limited supervision needed for this alternative approach is given by the users calling the SDS: each user can be seen as a partial Oracle who could confirm if a system prediction is right or wrong. We will illustrate on a real deployed SDS the efficiency of this cost-free method for the online adaptation of an SLU model.
Bibliographic reference. Gotab, Pierre / Damnati, Geraldine / Bechet, Frederic / Delphin-Poulat, Lionel (2010): "Online SLU model adaptation with a partial oracle", In INTERSPEECH-2010, 2862-2865.