Turning Flowchart into Dialog: Augmenting Flowchart-grounded Troubleshooting Dialogs via Synthetic Data Generation
This addresses data sparsity for researchers and developers building troubleshooting dialogue systems in specific domains like vehicles or laptops, but it is incremental as it builds on existing synthetic data generation methods.
The paper tackles the problem of scarce training data for flowchart-grounded troubleshooting dialogue systems by proposing a plan-based synthetic data generation approach, which improves downstream task performance, such as increasing flowchart path retrieval accuracy by up to 15% in out-of-flowchart settings.
Flowchart-grounded troubleshooting dialogue (FTD) systems, which follow the instructions 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 on flowcharts is costly, thus FTD systems are impeded by scarce training data. To mitigate the data sparsity issue, we propose a plan-based synthetic data generation (PlanSDG) approach that generates diverse synthetic dialog data at scale by transforming concise flowchart into dialogues. Specifically, its generative model employs a variational-base framework with a hierarchical planning strategy that includes global and local latent planning variables. Experiments on the FloDial dataset show that synthetic dialogue produced by PlanSDG improves the performance of downstream tasks, including flowchart path retrieval and response generation, in particular on the Out-of-Flowchart settings. In addition, further analysis demonstrate the quality of synthetic data generated by PlanSDG in paths that are covered by current sample dialogues and paths that are not covered.