论文标题
星：用于转移学习的模式引导的对话框数据集
STAR: A Schema-Guided Dialog Dataset for Transfer Learning
论文作者
论文摘要
我们介绍了Star，这是一个面向任务的对话框数据集，该数据集由13个域中的5,820个面向任务的对话框中的127,833个话语和知识基础查询组成，这些对话是特别旨在促进以任务为导向的对话框中的任务和域传输学习。此外，我们提出了一个可扩展的众包范式，以收集与Star相同质量的任意大型数据集。此外，我们介绍了新型架构引导的对话框模型，该模型使用对任务的明确描述从已知的任务概括到未知任务。我们证明了这些模型的有效性，尤其是对于跨任务和领域的零拍概括。
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.
