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We present STAR, a schema-guided task-oriented dialog dataset consisting of127,833 utterances and knowledge base queries across 5,820 task-orienteddialogs in 13 domains that is especially designed to facilitate task and domaintransfer learning in task-oriented dialog. Furthermore, we propose a scalablecrowd-sourcing paradigm to collect arbitrarily large datasets of the samequality as STAR. Moreover, we introduce novel schema-guided dialog models thatuse an explicit description of the task(s) to generalize from known to unknowntasks. We demonstrate the effectiveness of these models, particularly forzero-shot generalization across tasks and domains.

STAR: A Schema-Guided Dialog Dataset for Transfer Learning