Model Adaptation For Spoken Language Understanding

G. Tur
Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005.  
In this paper, we present a novel adaptation method for intent classification using Boosting in a spoken language understanding system. The goal is adapting an existing model to a new target application, which is similar but may have different intents or intent distributions. Adaptation can also be employed for a single application where the intent distribution varies by time. We assume the target application has a small amount of labeled data. We also propose employing active learning to
more » ... e learning to selectively sample the data to label for adaptation. Our results indicate that we can achieve the same intent classification accuracy using less than half of the labeled data when there is not much training data available. Furthermore, combined with active learning we see 18.6% relative reduction in classification error rate.
doi:10.1109/icassp.2005.1415045 dblp:conf/icassp/Tur05 fatcat:heatup3udjbttfmzspq7stiwhi