  In recent years, latent variable models, such as the Conditional Variational
Auto Encoder (CVAE), have been applied to both personalized and empathetic
dialogue generation. Prior work have largely focused on generating diverse
dialogue responses that exhibit persona consistency and empathy. However, when
it comes to the contextual coherence of the generated responses, there is still
room for improvement. Hence, to improve the contextual coherence, we propose a
novel Uncertainty Aware CVAE (UA-CVAE) framework. The UA-CVAE framework
involves approximating and incorporating the aleatoric uncertainty during
response generation. We apply our framework to both personalized and empathetic
dialogue generation. Empirical results show that our framework significantly
improves the contextual coherence of the generated response. Additionally, we
introduce a novel automatic metric for measuring contextual coherence, which
was found to correlate positively with human judgement.
