Automated Creation of an Intent Model for Conversational Agents

Abstract

ABSTRACT Conversational Agents (CA) are increasingly being deployed by organizations to provide round-the-clock support and to increase customer satisfaction. All CA have one thing in common despite the differences in their design: they need to be trained with users’ intents and corresponding training sentences. Access to proper data with acceptable coverage of intents and training sentences is a big challenge in CA deployment. Even with the access to the past conversations, the process of discovering intents and training sentences manually is not time and cost-effective. Here, an end to end automated framework that can discover intents and their training sentences in conversation logs to generate labeled data sets for training intent models is presented. The framework proposes different feature engineering techniques and leverages dimensionality reduction methods to assemble the features, then applies a density-based clustering algorithm iteratively to mine even the least common intents. Finally, the clustering results are automatically labeled by the final algorithm.