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. This is consistent with those in previous work like [@zhao2017learning], and confirms the advantage of latent variable methods for generation tasks over encoder-decoder models with multi-decoding strategy. 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.

  Methods              BLEU-prec   BLEU-recall   *distinct-1*   *distinct-2*
  -------------------- ----------- ------------- -------------- --------------
  SEQ2SEQ+BS           0.164       0.282         0.002          0.007
  SEQ2SEQ+noise(0.2)   0.163       0.288         0.003          0.014
  SEQ2SEQ+noise(0.5)   0.157       0.312         0.005          0.032
  SEQ2SEQ+noise(0.8)   0.153       0.320         0.007          0.065
  MMI-AntiLM           0.153       0.275         0.002          0.012
  KLA+WD               0.212       0.345         