Extracting structure information from dialogue data can help us better
understand user and system behaviors. In task-oriented dialogues, dialogue
structure has often been considered as transition graphs among dialogue states.
However, annotating dialogue states manually is expensive and time-consuming.
In this paper, we propose a simple yet effective approach for structure
extraction in task-oriented dialogues. We first detect and cluster possible
slot tokens with a pre-trained model to approximate dialogue ontology for a
target domain. Then we track the status of each identified token group and
derive a state transition structure. Empirical results show that our approach
outperforms unsupervised baseline models by far in dialogue structure
extraction. In addition, we show that data augmentation based on extracted
structures enriches the surface formats of training data and can achieve a
significant performance boost in dialogue response generation.

本文提出一种在面向任务的对话中提取结构化信息的方法，通过使用预训练模型检测和聚类可能的槽标记来逼近目标领域的对话本体，在此基础上推导出状态转移结构，实验结果表明这种方法在对话结构提取方面表现比无监督基线模型更好，此外，通过基于提取的结构进行数据增强可以大幅提高对话响应生成的性能。