precisely when the annotations are limited. In such situation, *distinct* measures the vocabulary a model actually uses and demonstrates its absolute lexical diversity. Example results will show this in next subsection. Furthermore, we observed that SEQ2SEQ+BS obtains the best BLEU-precision among all methods on EGOODS, but it performs much worse on Daily Dialog. Meanwhile, the BLEU-recall gap between SEQ2SEQ+BS and the best result on EGOODS is obviously small than that on DD. We point out that our dataset especially designed for “one source, multiple targets” problem significantly improves the generation quality of SEQ2SEQ methods.

  Methods              BLEU-prec   BLEU-recall   *distinct-1*   *distinct-2*
  -------------------- ----------- ------------- -------------- --------------
  SEQ2SEQ+BS           **0.379**   0.388         0.0012         0.0042
  SEQ2SEQ+noise(0.2)   **0.379**   0.386         0.0021         0.0146
  SEQ2SEQ+noise(0.5)   0.367       0.402         0.0029         0.0162
  SEQ2SEQ+noise(0.8)   0.347       0.395         0.003          **0.0420**
  MMI-AntiLM           0.356       0.374         0.0021         0.0146
  KLA+WD               0.373       **0.405**     0.0039         0.0216
  KLA+WD+BOW           0.374       0.404         0.0039         0.0231
  KLA+WD+SL            0.373       **0.405**     **0.0049**     0.0270

  : Results on EGOODS. The bottom 3 lines are CVAE based methods.[]{data-label="tab:egoods_result"}

### Human Evaluation on EGOODS

Human evaluation results on EGOODS are shown in Table \[tab:human\_eval\]. Such results show that our method achieves comparable fluency and coherence as baseline methods, but our diversity is much higher than other models.

  Methods      Fluency(%)   Coherence(%)   Diversity
  ------------ ------------ 