论文标题
角色扮演：概念设定的指导性个性化对话生成在两个聚会角色中
COSPLAY: Concept Set Guided Personalized Dialogue Generation Across Both Party Personas
论文作者
论文摘要
保持一致的角色对于建立类似人类的对话模型至关重要。但是，缺乏对伴侣的关注使模型更加自负：他们倾向于以一定的方式表现出自己的角色，例如僵硬地扭曲主题，将对话带到他们的利益上，无论如何，他们的角色都以对伴侣的好奇心而散发出自己的角色。在这项工作中，我们提出了角色扮演（概念设置了两个党派角色的个性化对话产生），将双方都视为“团队”：表达自我，同时对伴侣的好奇心，围绕相互角色的回应，并找到共同点。具体来说，我们首先在概念集中都代表自我伴侣，伴侣角色和相互对话。然后，我们建议使用一套知识增强操作的概念设置框架来处理它们，例如设置代数，设置扩展和设置距离。基于这些操作作为媒介，我们通过利用1）双方角色的概念，2）他们之间的概念关系和3）他们与未来对话的关系。在大型公共数据集《人物chat》上进行的广泛实验表明，我们的模型在自动和人类评估中都超出了最先进的基准，即产生较少的自我中心，更类似于人类和更高质量的反应。
Maintaining a consistent persona is essential for building a human-like conversational model. However, the lack of attention to the partner makes the model more egocentric: they tend to show their persona by all means such as twisting the topic stiffly, pulling the conversation to their own interests regardless, and rambling their persona with little curiosity to the partner. In this work, we propose COSPLAY(COncept Set guided PersonaLized dialogue generation Across both partY personas) that considers both parties as a "team": expressing self-persona while keeping curiosity toward the partner, leading responses around mutual personas, and finding the common ground. Specifically, we first represent self-persona, partner persona and mutual dialogue all in the concept sets. Then, we propose the Concept Set framework with a suite of knowledge-enhanced operations to process them such as set algebras, set expansion, and set distance. Based on these operations as medium, we train the model by utilizing 1) concepts of both party personas, 2) concept relationship between them, and 3) their relationship to the future dialogue. Extensive experiments on a large public dataset, Persona-Chat, demonstrate that our model outperforms state-of-the-art baselines for generating less egocentric, more human-like, and higher quality responses in both automatic and human evaluations.
