Dataflow Dialogue Generation
This work addresses dialogue generation for specific domains like MultiWOZ and SMCalFlow, with an incremental improvement in translation accuracy.
The paper tackles task-oriented dialogue generation in the dataflow dialogue paradigm, showing that using generated dialogues to augment training data improves the accuracy of translating user requests to dataflow expressions in the SMCalFlow domain.
We demonstrate task-oriented dialogue generation within the dataflow dialogue paradigm. We show an example of agenda driven dialogue generation for the MultiWOZ domain, and an example of generation without an agenda for the SMCalFlow domain, where we show an improvement in the accuracy of the translation of user requests to dataflow expressions when the generated dialogues are used to augment the translation training dataset.