Turning Flowchart into Dialog: Augmenting Flowchart-grounded
Troubleshooting Dialogs via Synthetic Data Generation
- URL: http://arxiv.org/abs/2305.01323v3
- Date: Sun, 29 Oct 2023 11:02:47 GMT
- Title: Turning Flowchart into Dialog: Augmenting Flowchart-grounded
Troubleshooting Dialogs via Synthetic Data Generation
- Authors: Haolan Zhan and Sameen Maruf and Lizhen Qu and Yufei Wang and Ingrid
Zukerman and Gholamreza Haffari
- Abstract summary: Flowchart-grounded troubleshooting dialogue (FTD) systems follow the instructions of a flowchart to diagnose users' problems in specific domains.
We propose a plan-based synthetic data generation approach that generates diverse synthetic dialog data at scale.
- Score: 50.06143883455979
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
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