  Mining the latent intentions from large volumes of natural language inputs is
a key step to help data analysts design and refine Intelligent Virtual
Assistants (IVAs) for customer service. To aid data analysts in this task we
present Verint Intent Manager (VIM), an analysis platform that combines
unsupervised and semi-supervised approaches to help analysts quickly surface
and organize relevant user intentions from conversational texts. For the
initial exploration of data we make use of a novel unsupervised and
semi-supervised pipeline that integrates the fine-tuning of high performing
language models, a distributed k-NN graph building method and community
detection techniques for mining the intentions and topics from texts. The
fine-tuning step is necessary because pre-trained language models cannot encode
texts to efficiently surface particular clustering structures when the target
texts are from an unseen domain or the clustering task is not topic detection.
For flexibility we deploy two clustering approaches: where the number of
clusters must be specified and where the number of clusters is detected
automatically with comparable clustering quality but at the expense of
additional computation time. We describe the application and deployment and
demonstrate its performance using BERT on three text mining tasks. Our
experiments show that BERT begins to produce better task-aware representations
using a labeled subset as small as 0.5% of the task data. The clustering
quality exceeds the state-of-the-art results when BERT is fine-tuned with
labeled subsets of only 2.5% of the task data. As deployed in the VIM
application, this flexible clustering pipeline produces high quality results,
improving the performance of data analysts and reducing the time it takes to
surface intentions from customer service data, thereby reducing the time it
takes to build and deploy IVAs in new domains.
