Variational Hierarchical Dialog Autoencoder for Dialog State Tracking Data Augmentation
Table 1: Results of data augmentation using VHDA for dialog state tracking on various datasets and state trackers. Note that we report inform accuracies for MultiWoZ datasets instead, as request-type prediction is trivial for these datasets.
['GDA', 'Model', 'WoZ2.0 Goal', 'WoZ2.0 Req', 'DSTC2 Goal', 'DSTC2 Req', 'MWoZ-R Goal', 'MWoZ-R Inf', 'MWoZ-H Goal', 'MWoZ-H Inf', 'DialEdit Goal', 'DialEdit Req']
Main Results. This subsection presents the results of augmenting state tracking datasets using synthetic samples generated from VHDA on various datasets and trackers The results strongly suggest that generative data augmentation for dialog state tracking is a viable strategy for improving existing DST models without modifying them, as improvements were observed at statistically significant levels regardless of the tracker and dataset.