Effective Data Augmentation Approaches to End-to-End Task-Oriented Dialogue
TABLE II: Experiment results on CamRest676 and KVRET.
['[EMPTY]', 'CamRest676', 'KVRET']
First, the results demonstrate that all the proposed data augmentation methods contribute to the significant improvements in F1 over the basic TSCP model. Except for the stop-word deletion method, all other methods perform better than the RL-enhanced TSCP. Second, the sentence-level augmentation methods are better than the word-level methods in most cases as the former provide more variances for user utterances. Third, the assembled augmentation, which combines all data generated by the four data augmentation methods, achieve the new state-of-the-art performance on the two datasets, with more than 2 points higher than the RL-enhanced TSCP model in terms of F1 score. It is clear to observe that machine utterance augmentation seriously deteriorates the performance. The reason for this may be that data augmentation introduces both variance and noise. However, the variance and noise in machine utterances will distract the system.