  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.
