Multi-Step Dialogue Workflow Action Prediction

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 multiturn 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 incontext learning leveraging retrieval and large language model prompting, and 3) zero-shot Figure 1: We propose the problem of multi-step Action graph traversal, which aggregates historical action State Tracking (AST), which involves predicting many sequences into a graph for prediction. We future workflow actions while prior work only predicts show that multi-step action prediction produces one step. We represent predictions as graphs that capture features that improve accuracy on downstream potential branching in future action sequences.