  Task oriented dialogue (TOD) requires the complex interleaving of a number of
individually controllable components with strong guarantees for explainability
and verifiability. This has made it difficult to adopt the multi-turn
multi-domain dialogue generation capabilities of streamlined end-to-end
open-domain dialogue systems. In this paper, we present a new framework,
DLGNet-Task, a unified task-oriented dialogue system which employs
autoregressive transformer networks such as DLGNet and GPT-2/3 to complete user
tasks in multi-turn multi-domain conversations. Our framework enjoys the
controllable, verifiable, and explainable outputs of modular approaches, and
the low development, deployment and maintenance cost of end-to-end systems.
Treating open-domain system components as additional TOD system modules allows
DLGNet-Task to learn the joint distribution of the inputs and outputs of all
the functional blocks of existing modular approaches such as, natural language
understanding (NLU), state tracking, action policy, as well as natural language
generation (NLG). Rather than training the modules individually, as is common
in real-world systems, we trained them jointly with appropriate module
separations. When evaluated on the MultiWOZ2.1 dataset, DLGNet-Task shows
comparable performance to the existing state-of-the-art approaches.
Furthermore, using DLGNet-Task in conversational AI systems reduces the level
of effort required for developing, deploying, and maintaining intelligent
assistants at scale.
