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。

STAR：用于迁移学习的基于模式的对话数据集

STAR: A Schema-Guided Dialog Dataset for Transfer Learning

We develop methods for detector learning which exploit joint training over
both weak and strong labels and which transfer learned perceptual
representations from strongly-labeled auxiliary tasks. Previous methods for
weak-label learning often learn detector models independently using latent
variable optimization, but fail to share deep representation knowledge across
classes and usually require strong initialization. Other previous methods
transfer deep representations from domains with strong labels to those with
only weak labels, but do not optimize over individual latent boxes, and thus
may miss specific salient structures for a particular category. We propose a
model that subsumes these previous approaches, and simultaneously trains a
representation and detectors for categories with either weak or strong labels
present. We provide a novel formulation of a joint multiple instance learning
method that includes examples from classification-style data when available,
and also performs domain transfer learning to improve the underlying detector
representation. Our model outperforms known methods on ImageNet-200 detection
with weak labels.

该研究开发了一种检测器学习方法，利用弱标签和强标签的联合训练，并从带有强标签的辅助任务中传递学习到的感知表示，同时训练具有弱标签或强标签的类别的表示和检测器。