  Intent understanding plays an important role in dialog systems, and is
typically formulated as a supervised classification problem. However, it is
challenging and time-consuming to design the intent labels manually to support
a new domain. This paper proposes an unsupervised two-stage approach to
discover intents and generate meaningful intent labels automatically from a
collection of unlabeled utterances. In the first stage, we aim to generate a
set of semantically coherent clusters where the utterances within each cluster
convey the same intent. We obtain the utterance representation from various
pre-trained sentence embeddings and present a metric of balanced score to
determine the optimal number of clusters in K-means clustering. In the second
stage, the objective is to generate an intent label automatically for each
cluster. We extract the ACTION-OBJECT pair from each utterance using a
dependency parser and take the most frequent pair within each cluster, e.g.,
book-restaurant, as the generated cluster label. We empirically show that the
proposed unsupervised approach can generate meaningful intent labels
automatically and achieves high precision and recall in utterance clustering
and intent discovery.
