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。

DSTC8 中的基于模式引导的对话状态跟踪任务

Schema-Guided Dialogue State Tracking Task at DSTC8

This paper describes our approach in DSTC 8 Track 4: Schema-Guided Dialogue
State Tracking. The goal of this task is to predict the intents and slots in
each user turn to complete the dialogue state tracking (DST) based on the
information provided by the task's schema. Different from traditional
stage-wise DST, we propose an end-to-end DST system to avoid error accumulation
between the dialogue turns. The DST system consists of a machine reading
comprehension (MRC) model for non-categorical slots and a Wide & Deep model for
categorical slots. As far as we know, this is the first time that MRC and Wide
& Deep model are applied to DST problem in a fully end-to-end way. Experimental
results show that our framework achieves an excellent performance on the test
dataset including 50% zero-shot services with a joint goal accuracy of 0.8652
and a slot tagging F1-Score of 0.9835.

本文介绍了 DSTC 8 轨道 4：基于模式的对话状态跟踪的方法以及测试结果。我们提出了一种端到端的 DST 系统，该系统包括针对非分类插槽的 MRC 模型和对于分类插槽的 Wide & Deep 模型。通过实验，证明我们的方法在测试数据集中得到了优异的性能。