  We present STAR, a schema-guided task-oriented dialog dataset consisting of
127,833 utterances and knowledge base queries across 5,820 task-oriented
dialogs in 13 domains that is especially designed to facilitate task and domain
transfer learning in task-oriented dialog. Furthermore, we propose a scalable
crowd-sourcing paradigm to collect arbitrarily large datasets of the same
quality as STAR. Moreover, we introduce novel schema-guided dialog models that
use an explicit description of the task(s) to generalize from known to unknown
tasks. We demonstrate the effectiveness of these models, particularly for
zero-shot generalization across tasks and domains.
