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。