Daniel Boies

An overview of end-to-end language understanding and dialog management for personal digital assistants

Spoken language understanding and dialog management have emerged as key technologies in interacting with personal digital assistants (PDAs). The coverage, complexity, and the scale of PDAs are much larger than previous conversational understanding systems. As such, new problems arise. In this paper, we provide an overview of the language understanding and dialog management capabilities of PDAs, focusing particularly on Cortana, Microsofts PDA. We explain the system architecture for language understanding and dialog management for our PDA, indicate how it differs with prior state-of-the-art systems, and describe key components. We also report a set of experiments detailing system performance on a variety of scenarios and tasks. We describe how the quality of user experiences are measured end-to-end and also discuss open issues.

Task Completion Platform: A self-serve multi-domain goal oriented dialogue platform

We demonstrate the Task Completion Platform (TCP); a multi-domain dialogue platform that can host and execute large numbers of goal-orientated dialogue tasks. The platform features a task configuration language, TaskForm, that allows the definition of each individual task to be decoupled from the overarching dialogue policy used by the platform to complete those tasks. This separation allows for simple and rapid authoring of new tasks, while dialogue policy and platform functionality evolve independent of the tasks. The current platform includes machine learnt models that provide contextual slot carry-over, flexible item selection, and task selection/switching. Any new task immediately gains the benefit of these pieces of built-in platform functionality. The platform is used to power many of the multi-turn dialogues supported by the Cortana personal assistant.