Dialog State Tracking with Reinforced Data Augmentation
Table 3: Ablation study of performances on the test set of WoZ and MultiWoZ.
['Setting', 'WoZ', 'Multi']
To evaluate the performance of different level rewards, we perform ablation study with GLAD⋆ on both the WoZ and MultiWoz datasets. From the table we can see that both rewards can provide the improvements of 1% to 2% in the datasets and the bag-level reward achieves larger gains than the instance-level reward. Compared with DA setting, RDA obtains the improvements of 3% to 4% on the datasets by combining the both rewards, which indicates that the summation reward is more reliable for policy learning than individual ones.