This paper gives an overview of the Schema-Guided Dialogue State Tracking
task of the 8th Dialogue System Technology Challenge. The goal of this task is
to develop dialogue state tracking models suitable for large-scale virtual
assistants, with a focus on data-efficient joint modeling across domains and
zero-shot generalization to new APIs. This task provided a new dataset
consisting of over 16000 dialogues in the training set spanning 16 domains to
highlight these challenges, and a baseline model capable of zero-shot
generalization to new APIs. Twenty-five teams participated, developing a range
of neural network models, exceeding the performance of the baseline model by a
very high margin. The submissions incorporated a variety of pre-trained
encoders and data augmentation techniques. This paper describes the task
definition, dataset and evaluation methodology. We also summarize the approach
and results of the submitted systems to highlight the overall trends in the
state-of-the-art.

本文介绍了第 8 届对话系统技术挑战赛的 Schema-Guided 对话状态跟踪任务，其目标是开发适用于大型虚拟助手的对话状态跟踪模型，重点是跨域的数据高效联合建模和新 API 的零 - shot 推广。本任务提供了一个包含 16 个领域超过 16000 个对话的数据集，其中包括一个能够零 - shot 推广到新 API 的基线模型。本文介绍了任务定义、数据集和评估方法，并概述了提交系统的方法和结果，以突出现今最先进技术的总体趋势。