A proposal for Developing and Deploying Statistical Dialog Management in Commercial Conversational Platforms

Abstract

Conversational interfaces have recently become ubiquitous in the personal sphere by improving an individual’s quality of life and industrial environments by automating services and their corre- sponding cost savings. However, designing the dialog model used by these interfaces to decide the following response is a hard-to-accomplish task for complex conversational interactions. This paper proposes a statistical-based dialog manager architecture, which provides flexibility to develop and maintain this module. Our proposal has been integrated using DialogFlow, a natural language understanding platform provided by Google to design conversational user interfaces. The proposed hybrid architecture has been assessed with a real use case for a train scheduling domain, proving that the user experience is highly valued and can be integrated into commercial setups