The design of dialogue flows is a critical but time-consuming task when developing task-oriented dialogue (TOD) systems. We propose an approach for the unsupervised discovery of flows from dialogue history, thus making the process applicable to any domain for which such an history is available. Briefly, utterances are represented in a vector space and clustered according to their semantic similarity. Clusters, which can be seen as dialogue states, are then used as the vertices of a transition graph for representing the flows visually. We present concrete examples of flows, discovered from MultiWOZ, a public TOD dataset. We further elaborate on their significance and relevance for the underlying conversations and introduce an automatic validation metric for their assessment. Experimental results demonstrate the potential of the proposed approach for extracting meaningful flows from task-oriented conversations.

翻译：对话流程的设计是开发面向任务型对话系统时一项关键但耗时的任务。我们提出了一种从对话历史中无监督发现流程的方法，从而使得该过程适用于任何具备历史记录的领域。简言之，将话语表示在向量空间中并根据其语义相似性进行聚类。这些可被视为对话状态的聚类，随后被用作转移图的顶点，以可视化地表示流程。我们展示了从公开TOD数据集MultiWOZ中发现的流程的具体案例，进一步阐述了它们对底层对话的重要性和相关性，并引入了一种自动验证指标用于评估。实验结果表明，所提方法在从面向任务型对话中提取有意义流程方面具有潜力。