  Conversational systems are of primary interest in the AI community. Chatbots
are increasingly being deployed to provide round-the-clock support and to
increase customer engagement. Many of the commercial bot building frameworks
follow a standard approach that requires one to build and train an intent model
to recognize a user input. Intent models are trained in a supervised setting
with a collection of textual utterance and intent label pairs. Gathering a
substantial and wide coverage of training data for different intent is a
bottleneck in the bot building process. Moreover, the cost of labeling a
hundred to thousands of conversations with intent is a time consuming and
laborious job. In this paper, we present an intent discovery framework that
involves 4 primary steps: Extraction of textual utterances from a conversation
using a pre-trained domain agnostic Dialog Act Classifier (Data Extraction),
automatic clustering of similar user utterances (Clustering), manual annotation
of clusters with an intent label (Labeling) and propagation of intent labels to
the utterances from the previous step, which are not mapped to any cluster
(Label Propagation); to generate intent training data from raw conversations.
We have introduced a novel density-based clustering algorithm ITER-DBSCAN for
unbalanced data clustering. Subject Matter Expert (Annotators with domain
expertise) manually looks into the clustered user utterances and provides an
intent label for discovery. We conducted user studies to validate the
effectiveness of the trained intent model generated in terms of coverage of
intents, accuracy and time saving concerning manual annotation. Although the
system is developed for building an intent model for the conversational system,
this framework can also be used for a short text clustering or as a labeling
framework.
