0.010          0.041
  KLA+WD+BOW           0.210       0.344         0.013          0.066
  KLA+WD+SL            **0.214**   **0.354**     **0.014**      **0.078**

  : Results on Daily Dialog (DD). The bottom 3 lines are CVAE based methods. []{data-label="tab:dd_result"}

### Automatic Quantitative Measurement on EGOODS

To better study current methods on the “one source, multiple targets” problem, experiments are conducted on our newly collected native *one-to-many* dataset EGOODS. Performances of different methods are shown in Table. \[tab:egoods\_result\]. First of all, our method achieves comparable accuracy with baselines and best diversity among all methods with an only exception of SEQ2SEQ+noise(0.8) on *distinct-2*. This demonstrates the effectiveness of SLCVAE on the *one-to-many* data. Although SEQ2SEQ+noise(0.8) gets the best *distinct-2*, its precision is sacrificed significantly due to the nosie, which means the results tend to be meaningless. In detail, our method harvests the much better gains on word-level diversity while is only slightly better than CVAE on BLEU-recall. We explain this in two folds: First, strong baselines can benefit from the large scale and *one-to-many* nature of EGOODS to better fit the multiple targets. Another reason is that automatically evaluating the quality of generated texts is very challenging. BLEU-recall only measures the coverage of hypothesis for the annotated targets, and could not judge good algorithms 