  Non-task-oriented dialog models suffer from poor quality and non-diverse
responses. To overcome limited conversational data, we apply Simulated Multiple
Reference Training (SMRT; Khayrallah et al., 2020), and use a paraphraser to
simulate multiple responses per training prompt. We find SMRT improves over a
strong Transformer baseline as measured by human and automatic quality scores
and lexical diversity. We also find SMRT is comparable to pretraining in human
evaluation quality, and outperforms pretraining on automatic quality and
lexical diversity, without requiring related-domain dialog data.
