  Building the Natural Language Understanding (NLU) modules of task-oriented
Spoken Dialogue Systems (SDS) involves a definition of intents and entities,
collection of task-relevant data, annotating the data with intents and
entities, and then repeating the same process over and over again for adding
any functionality/enhancement to the SDS. In this work, we showcase an Intent
Bulk Labeling system where SDS developers can interactively label and augment
training data from unlabeled utterance corpora using advanced clustering and
visual labeling methods. We extend the Deep Aligned Clustering work with a
better backbone BERT model, explore techniques to select the seed data for
labeling, and develop a data balancing method using an oversampling technique
that utilizes paraphrasing models. We also look at the effect of data
augmentation on the clustering process. Our results show that we can achieve
over 10% gain in clustering accuracy on some datasets using the combination of
the above techniques. Finally, we extract utterance embeddings from the
clustering model and plot the data to interactively bulk label the samples,
reducing the time and effort for data labeling of the whole dataset
significantly.
