The present disclosure relates generally to natural language understanding. An exemplary computer-enabled method comprises: receiving first utterance data corresponding to a first plurality of intents; identifying a first plurality of intent clusters using an unsupervised machine-learning algorithm, wherein each intent cluster of the first plurality of intent clusters comprises a respective subset of the first plurality of intents; training, based on the first utterance data, an intent cluster classification model, wherein the intent cluster classification model is configured to receive a user utterance and identify an intent cluster of the first plurality of intent clusters; and training, based on the first utterance data, an intent classification model for each intent cluster of the first plurality of intent clusters to obtain a plurality of intent classification models, wherein each intent classification model is configured to receive the user utterance and identify an intent from the respective intent cluster.
UNSUPERVISED CLUSTERING OF INTENTS FOR NATURAL LANGUAGE UNDERSTANDING