BLEU-recall, *distinct-1* and *distinct-2* are reported as diversity measures.

![Example of generated texts. Despite similar coverage for references, SLCAVE has better diversity in vocabulary and expressions.](showcase_en){width="0.95\linewidth"}

\[fig:show\_case\]

We also conduct human evaluations on the EGOODS dataset. 7 human experts are employed to measure the fluency of generated sentences, coherence of each sentence to source and diversity. For fluency and coherence, experts are asked to vote to each sentence. Sentences which yield more than 4 votes are good sentences. The ratio of good sentences are reported. For diversity, 5 level of diverse scores are introduced. The higher the score, the more diverse the sentence is. The final diversity score of each sentence is the average score of all experts.

### Automatic Quantitative Measurement on Daily Dialog

Table. \[tab:dd\_result\] shows the evaluation results of all methods on Daily Dialog dataset. 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 