  We present a new method SOLOIST that uses transfer learning and machine
teaching to build task bots at scale. We parameterize classical modular
task-oriented dialog systems using a Transformer-based auto-regressive language
model, which subsumes different dialog modules into a single neural model. We
pre-train, on heterogeneous dialog corpora, a task-grounded response generation
model, which can generate dialog responses grounded in user goals and
real-world knowledge for task completion. The pre-trained model can be
efficiently adapted to accomplish new tasks with a handful of task-specific
dialogs via machine teaching, where training samples are generated by human
teachers interacting with the system. Experiments show that (i) SOLOIST creates
new state-of-the-art on well-studied task-oriented dialog benchmarks, including
CamRest676 and MultiWOZ; (ii) in the few-shot fine-tuning settings, SOLOIST
significantly outperforms existing methods, and (iii) the use of machine
teaching substantially reduces the labeling cost of fine-tuning. The
pre-trained models and codes are available at https://aka.ms/soloist.
