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We present a new method SOLOIST that uses transfer learning and machineteaching to build task bots at scale. We parameterize classical modulartask-oriented dialog systems using a Transformer-based auto-regressive languagemodel, which subsumes different dialog modules into a single neural model. Wepre-train, on heterogeneous dialog corpora, a task-grounded response generationmodel, which can generate dialog responses grounded in user goals andreal-world knowledge for task completion. The pre-trained model can beefficiently adapted to accomplish new tasks with a handful of task-specificdialogs via machine teaching, where training samples are generated by humanteachers interacting with the system. Experiments show that (i) SOLOIST createsnew state-of-the-art on well-studied task-oriented dialog benchmarks, includingCamRest676 and MultiWOZ; (ii) in the few-shot fine-tuning settings, SOLOISTsignificantly outperforms existing methods, and (iii) the use of machineteaching substantially reduces the labeling cost of fine-tuning. Thepre-trained models and codes are available at https://aka.ms/soloist.

SOLOIST: Building Task Bots at Scale with Transfer Learning and Machine Teaching