  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.
