Conditional Variational AutoEncoder (CVAE) effectively increases the
diversity and informativeness of responses in open-ended dialogue generation
tasks through enriching the context vector with sampled latent variables.
However, due to the inherent one-to-many and many-to-one phenomena in human
dialogues, the sampled latent variables may not correctly reflect the contexts'
semantics, leading to irrelevant and incoherent generated responses. To resolve
this problem, we propose Self-separated Conditional Variational AutoEncoder
(abbreviated as SepaCVAE) that introduces group information to regularize the
latent variables, which enhances CVAE by improving the responses' relevance and
coherence while maintaining their diversity and informativeness. SepaCVAE
actively divides the input data into groups, and then widens the absolute
difference between data pairs from distinct groups, while narrowing the
relative distance between data pairs in the same group. Empirical results from
automatic evaluation and detailed analysis demonstrate that SepaCVAE can
significantly boost responses in well-established open-domain dialogue
datasets.

通过引入自身分离的条件变分自编码器（SepaCVAE），将组信息引入到规范化的潜变量中，进一步增强了 CVAE 通过提高响应的相关性和连贯性同时保持它们的多样性和信息量在开放领域对话生成任务中的性能。