  Complex dialogue mappings (CDM), including one-to-many and many-to-one
mappings, tend to make dialogue models generate incoherent or dull responses,
and modeling these mappings remains a huge challenge for neural dialogue
systems. To alleviate these problems, methods like introducing external
information, reconstructing the optimization function, and manipulating data
samples are proposed, while they primarily focus on avoiding training with CDM,
inevitably weakening the model's ability of understanding CDM in human
conversations and limiting further improvements in model performance. This
paper proposes a Sentence Semantic \textbf{Seg}mentation guided
\textbf{C}onditional \textbf{V}ariational \textbf{A}uto-\textbf{E}ncoder
(SegCVAE) method which can model and take advantages of the CDM data.
Specifically, to tackle the incoherent problem caused by one-to-many, SegCVAE
uses response-related prominent semantics to constrained the latent variable.
To mitigate the non-diverse problem brought by many-to-one, SegCVAE segments
multiple prominent semantics to enrich the latent variables. Three novel
components, Internal Separation, External Guidance, and Semantic Norms, are
proposed to achieve SegCVAE. On dialogue generation tasks, both the automatic
and human evaluation results show that SegCVAE achieves new state-of-the-art
performance.
