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.
翻译:保持一个一致的人性对于建立人性式对话模式至关重要。 但是,缺乏对伴侣的关注使得模型更加以自我为中心:他们往往以各种手段展示自己的个人性,例如用强硬的手法扭曲话题,将对话引向他们自己的利益,将交谈引向他们的利益,用一点好奇心向伙伴倾斜。在这项工作中,我们提议COSPLAY(Concept Set 指导性人性化对话生成,横跨两部分)将双方视为“团队 ” : 表达自我性,同时保持对伴侣的好奇心,引导对彼此的反应,并找到共同点。 具体地说,我们首先在概念组中代表自我、伙伴和相互对话。然后,我们提出“概念设置框架”框架,并配有一套知识强化的操作,例如设置代数,设置扩展,并设定距离。我们以这些操作为介于中,我们用1个政党概念来表达自我,2个概念关系,3个概念与未来对话的关系。我们首先在概念组中代表自我、伙伴、伙伴和相互对话中的所有方性,然后在大质量模型上进行广泛的实验,以更高级的模型展示,在更高级的模型上展示,在更低的模型中, 立为人类的立为人类的基底的模型。