  This article presents a hybrid approach based on a Grounded Text Generation
(GTG) model to building robust task bots at scale. GTG is a hybrid model which
uses a large-scale Transformer neural network as its backbone, combined with
symbol-manipulation modules for knowledge base inference and prior knowledge
encoding, to generate responses grounded in dialog belief state and real-world
knowledge for task completion. GTG is pre-trained on large amounts of raw text
and human conversational data, and can be fine-tuned to complete a wide range
of tasks.
  The hybrid approach and its variants are being developed simultaneously by
multiple research teams. The primary results reported on task-oriented dialog
benchmarks are very promising, demonstrating the big potential of this
approach. This article provides an overview of this progress and discusses
related methods and technologies that can be incorporated for building robust
conversational AI systems.
