Multi-turn conversations consist of complex semantic structures, and it is
still a challenge to generate coherent and diverse responses given previous
utterances. It's practical that a conversation takes place under a background,
meanwhile, the query and response are usually most related and they are
consistent in topic but also different in content. However, little work focuses
on such hierarchical relationship among utterances. To address this problem, we
propose a Conversational Semantic Relationship RNN (CSRR) model to construct
the dependency explicitly. The model contains latent variables in three
hierarchies. The discourse-level one captures the global background, the
pair-level one stands for the common topic information between query and
response, and the utterance-level ones try to represent differences in content.
Experimental results show that our model significantly improves the quality of
responses in terms of fluency, coherence and diversity compared to baseline
methods.

本文介绍了使用 Conversational Semantic Relationship RNN 模型解决多轮对话中相关性和差异性的挑战，模型包含三个层级：言语层面、对话层面和发言层面，能够识别全局背景和主题信息，并提高了响应的连贯性和多样性。