Improve Diverse Text Generation by Self Labeling Conditional Variational Auto Encoder
Table 1: Results on Daily Dialog (DD). The bottom 3 lines are CVAE based methods.
['Methods', 'BLEU-prec', 'BLEU-recall', '[ITALIC] distinct-1', '[ITALIC] distinct-2']
Results for SEQ2SEQ-noise with different standard deviations are also listed. Training skills of KLA and WD are used for all CVAE based methods. We can see that our proposed method outperforms all baselines in terms of all the 4 metrics on this task. And it is worth noting that our method obtains much higher diversity measures no matter in discourse-level or word-level than all others. In the meanwhile, the accuracy metrics BLEU-precision of our method remains slightly better than the best baseline. This confirms our insight of the generating process that our labeling objective can lead to an equilibrium at which the KL-vanishing problem is significantly relieved and so result in better diversity. Remind that Daily Dialog is actually a one-to-one dataset. The better performance in diversity on DD demonstrates that our model can better exploit such training data without explicit one-to-many annotations. Further, the results show that CVAE based models beat conventional encoder-decoder methods in almost all metrics. In addition, we find that with growing noise, the accuracy of SEQ2SEQ+noise decreases while the diversity increases significantly. Especially when the noise is small (e.g. 0.2), the diversity has obvious gains with only a slightly sacrifice on accuracy.