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Flowchart-grounded troubleshooting dialogue (FTD) systems, which follow theinstructions of a flowchart to diagnose users' problems in specific domains(e.g., vehicle, laptop), have been gaining research interest in recent years.However, collecting sufficient dialogues that are naturally grounded onflowcharts is costly, thus FTD systems are impeded by scarce training data. Tomitigate the data sparsity issue, we propose a plan-based synthetic datageneration (PlanSDG) approach that generates diverse synthetic dialog data atscale by transforming concise flowchart into dialogues. Specifically, itsgenerative model employs a variational-base framework with a hierarchicalplanning strategy that includes global and local latent planning variables.Experiments on the FloDial dataset show that synthetic dialogue produced byPlanSDG improves the performance of downstream tasks, including flowchart pathretrieval and response generation, in particular on the Out-of-Flowchartsettings. In addition, further analysis demonstrate the quality of syntheticdata generated by PlanSDG in paths that are covered by current sample dialoguesand paths that are not covered.

Turning Flowchart into Dialog: Augmenting Flowchart-grounded Troubleshooting Dialogs via Synthetic Data Generation