Improving Contextual Coherence in Variational Personalized and Empathetic Dialogue Agents
This work addresses a specific bottleneck in dialogue systems for applications requiring personalization and empathy, representing an incremental improvement over prior methods.
The paper tackles the problem of poor contextual coherence in personalized and empathetic dialogue generation by proposing an Uncertainty Aware CVAE framework, which significantly improves coherence and introduces a new automatic metric that correlates with human judgment.
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.