论文标题
改善各种个性化和善解人意的对话代理的上下文连贯性
Improving Contextual Coherence in Variational Personalized and Empathetic Dialogue Agents
论文作者
论文摘要
近年来，潜在变量模型（例如条件变异自动编码器（CVAE））已应用于个性化和善解人意的对话。先前的工作主要集中在产生表现出角色一致性和同理心的各种对话响应上。但是，当涉及到生成的响应的上下文连贯性时，仍然存在改进的余地。因此，为了提高上下文连贯性，我们提出了一种新颖的不确定性CVAE（UA-CVAE）框架。 UA-CVAE框架涉及在响应产生过程中近似和纳入抗生气的不确定性。我们将框架应用于个性化和善解人意的对话生成。经验结果表明，我们的框架显着提高了生成的响应的上下文连贯性。此外，我们引入了一种新型的自动指标，用于测量上下文连贯性，发现该指标与人类判断呈正相关。
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.
