constructed one-to-many dataset EGOODS is adopted in the second task.

### Baselines

We compared our SLCVAE to 4 strong baselines: SEQ2SEQ [@sutskever2014sequence], MMI-AntiLM [@li2016diversity], CVAE and CVAE with *bag-of-word loss* (CVAE+BOW) [@zhao2017learning]. Several training skills, such as *KL-annealing*(KLA) and *word dropout*(WD) [@bowman2016generating], are used in combination with baselines and our method to improve the performance. All methods are required to generate 10 responses for each given input.

Note that although the SEQ2SEQ model uses deterministic encoding vectors, the widely adopted beam search strategy can be applied during inference procedure to generate 10-best decoding results which corresponds to 10 responses (denoted as SEQ2SEQ-BS). MMI-AntiLM method follows this idea and put MMI prior onto the beam search strategy. We tried different beam size from 10 to 100 and find that beam size set to 10 gives the best result for all dataset. It is worth noting that beam search is applied only to above two methods and will bring unfair advantages due to the exploration of a much larger search space. All other methods including ours use the greedy strategy during decoding to be consistent with previous work. We also tried another simple strategy that adds a random noise drawn from a 