Variational Hierarchical Dialog Autoencoder for Dialog State Tracking Data Augmentation
Table 3: Ablation studies on the training techniques using GCE+ as the tracker. The effect of different dropout schemes and training objectives is quantified. MIM stands for mutual information maximization (Section 3.5).
['Drop.', 'Obj.', '[BOLD] z( [ITALIC] c)-KL', 'WoZ2.0 Goal', 'WoZ2.0 Req']
The Effect of Employing Training Techniques. To demonstrate the effectiveness of the two proposed training techniques, we compare (1) the data augmentation results and (2) the KL-divergence between the posterior and prior of the dialog latents z(c) The results support our hypothesis that the proposed measures reduces the risk of inference collapse, as evident from high KL-divergence values and stronger data augmentation benefits.