  In task-oriented dialogue, a system often needs to follow a sequence of
actions, called a workflow, that complies with a set of guidelines in order to
complete a task. In this paper, we propose the novel problem of multi-step
workflow action prediction, in which the system predicts multiple future
workflow actions. Accurate prediction of multiple steps allows for multi-turn
automation, which can free up time to focus on more complex tasks. We propose
three modeling approaches that are simple to implement yet lead to more action
automation: 1) fine-tuning on a training dataset, 2) few-shot in-context
learning leveraging retrieval and large language model prompting, and 3)
zero-shot graph traversal, which aggregates historical action sequences into a
graph for prediction. We show that multi-step action prediction produces
features that improve accuracy on downstream dialogue tasks like predicting
task success, and can increase automation of steps by 20% without requiring as
much feedback from a human overseeing the system.
