论文标题
具有隐式用户角色检测的个性化对话生成器
A Personalized Dialogue Generator with Implicit User Persona Detection
论文作者
论文摘要
当前的个性化对话中的当前作品主要有助于代理人表现出一致的个性并推动更有用的回应。但是，我们发现大多数以前模型的生成的响应往往是以自我为中心的，对话中的用户几乎没有照顾。此外，我们认为类似人类的对话基本上是基于推断有关另一方角色的信息而构建的。在此激励的情况下，我们通过检测隐性用户角色提出了一种新颖的个性化对话生成器。因为很难为每个用户收集大量详细的角色，所以我们试图在没有外部知识的情况下对用户的潜在角色及其对话历史记录进行建模。使用条件变异推断构想了感知和推子变量。这两个潜在变量模拟了人们意识到彼此角色并在对话中产生相应表达的过程。最后，提出了后歧视的正规化以增强训练程序。实证研究表明，与最先进的方法相比，我们的方法更关心用户的角色，并在整个评估中实现了可观的提升。
Current works in the generation of personalized dialogue primarily contribute to the agent presenting a consistent personality and driving a more informative response. However, we found that the generated responses from most previous models tend to be self-centered, with little care for the user in the dialogue. Moreover, we consider that human-like conversation is essentially built based on inferring information about the persona of the other party. Motivated by this, we propose a novel personalized dialogue generator by detecting an implicit user persona. Because it is hard to collect a large number of detailed personas for each user, we attempted to model the user's potential persona and its representation from dialogue history, with no external knowledge. The perception and fader variables were conceived using conditional variational inference. The two latent variables simulate the process of people being aware of each other's persona and producing a corresponding expression in conversation. Finally, posterior-discriminated regularization was presented to enhance the training procedure. Empirical studies demonstrate that, compared to state-of-the-art methods, our approach is more concerned with the user's persona and achieves a considerable boost across the evaluations.
