Workflow Discovery from Dialogues in the Low Data Regime

Text-based dialogues are now widely used to solve real-world problems. In cases where solution strategies are already known, they can sometimes be codified into workflows and used to guide humans or artificial agents through the task of helping clients. In this work, we introduce a new problem formulation that we call Workflow Discovery (WD) in which we are interested in the situation where a formal workflow may not yet exist. Still, we wish to discover the set of actions that have been taken to resolve a particular problem. We also examine a sequence-to-sequence (Seq2Seq) approach for this novel task using multiple Seq2Seq models. We present experiments where we extract workflows from dialogues in the Action-Based Conversations Dataset (ABCD) and the MultiWOZ dataset. We propose and evaluate an approach that conditions models on the set of possible actions, and we show that using this strategy, we can improve WD performance in the out-of-distribution setting. Further, on ABCD a modified variant of our Seq2Seq method achieves state-of-the-art performance on related but different tasks of Action State Tracking (AST) and Cascading Dialogue Success (CDS) across many evaluation metrics.