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.

我们提出了 STAR，这是一个包含 127,833 个话语和基于知识库的查询，涵盖 13 个领域，共 5820 个任务型对话的模式引导数据集，旨在促进任务和领域的迁移学习。此外，我们提出了一种可扩展的众包范例，用于收集与 STAR 质量相同的任意大的数据集。此外，我们引入了新颖的模式引导对话模型，通过对任务的明确描述将从已知的任务泛化到未知的任务，证明了这些模型的有效性，尤其是在任务和领域的零 - shot 推广方面。