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.

翻译：复杂的对话映射(CDM),包括一对一对一和多对一的映射(CDM),往往使对话模型产生不连贯或乏味的响应,而这些映射模型对于神经对话系统来说仍然是一项巨大的挑战。为了缓解这些问题,提出了采用外部信息、重建优化功能和调控数据样本等方法,而这些方法主要侧重于避免清洁发展机制培训,不可避免地削弱模型在人际对话中理解清洁发展机制的能力,并限制模型性能的进一步改进。本文件建议采用一个句式语义\ textbf{Sef{Segionation 指导的\ textbf{C}Onditionalbf{V}这些映射模型对于神经对话系统来说仍是一个巨大的挑战。为了减轻由多至Cnext-Textbf{A}Outomicalal 带来的非反常问题,Segreal-Developleal-Developleal-Developmental-Developmental-deal-deal-deal-Deal-Developal-deal-deal-deal-deal-deal-deal-deal-deal-de-de-deal-deal-deal-deal-deal-deal-deal-deal-deal-deal-deal-deal-deal-de-de-deal-deal-deal-deal-deal-deal-deal-deal-deal-deal-deal-deal-deal-deal-deal)部分,这是提议的多重任务,Stamental 和Stamental 和Stail-Stail-S,Slo-de-de-de,Slo-Devel-Devel-deal-deal-deal-de-Deal 显示,S-S-Deal-deal-deal-deal-deal-deal-deal-de-deal-deal-de-de-deal-deal-de,S,S-de-de-de-S,S,S,S-S-S-S-S,S,S,S,S-S-S-S-S-S-de-de-