Tracking dialogue states is an essential topic in task-oriented dialogue
systems, which involve filling in the necessary information in pre-defined
slots corresponding to a schema. While general pre-trained language models have
been shown effective in slot-filling, their performance is limited when applied
to specific domains. We propose a graph-based framework that learns
domain-specific prompts by incorporating the dialogue schema. Specifically, we
embed domain-specific schema encoded by a graph neural network into the
pre-trained language model, which allows for relations in the schema to guide
the model for better adaptation to the specific domain. Our experiments
demonstrate that the proposed graph-based method outperforms other multi-domain
DST approaches while using similar or fewer trainable parameters. We also
conduct a comprehensive study of schema graph architectures, parameter usage,
and module ablation that demonstrate the effectiveness of our model on
multi-domain dialogue state tracking.

我们提出了一种基于图的框架，通过将对话模式编码为图神经网络来嵌入预训练语言模型，从而在特定领域上对模型进行了更好地自适应，实验证明该方法在多领域对话状态跟踪方面优于其他方法。

多领域对话状态跟踪的模式图引导提示

Schema Graph-Guided Prompt for Multi-Domain Dialogue State Tracking

To advance multi-domain (cross-domain) dialogue modeling as well as alleviate
the shortage of Chinese task-oriented datasets, we propose CrossWOZ, the first
large-scale Chinese Cross-Domain Wizard-of-Oz task-oriented dataset. It
contains 6K dialogue sessions and 102K utterances for 5 domains, including
hotel, restaurant, attraction, metro, and taxi. Moreover, the corpus contains
rich annotation of dialogue states and dialogue acts at both user and system
sides. About 60% of the dialogues have cross-domain user goals that favor
inter-domain dependency and encourage natural transition across domains in
conversation. We also provide a user simulator and several benchmark models for
pipelined task-oriented dialogue systems, which will facilitate researchers to
compare and evaluate their models on this corpus. The large size and rich
annotation of CrossWOZ make it suitable to investigate a variety of tasks in
cross-domain dialogue modeling, such as dialogue state tracking, policy
learning, user simulation, etc.

本研究提出 CrossWOZ 数据集，这是中国首个大规模的跨领域 Wizard-of-Oz 任务导向数据集，包含了酒店、餐厅、景点、地铁和出租车等 5 个领域的 6k 对话会话和 102k 含有丰富对话状态和对话行为标注的发言。同时，数据集提供了用户模拟器和一些基准模型，方便研究人员在该数据集上比较和评估他们的模型。