  Human-like chit-chat conversation requires agents to generate responses that
are fluent, engaging and consistent. We propose Sketch-Fill-A-R, a framework
that uses a persona-memory to generate chit-chat responses in three phases.
First, it generates dynamic sketch responses with open slots. Second, it
generates candidate responses by filling slots with parts of its stored persona
traits. Lastly, it ranks and selects the final response via a language model
score. Sketch-Fill-A-R outperforms a state-of-the-art baseline both
quantitatively (10-point lower perplexity) and qualitatively (preferred by 55%
heads-up in single-turn and 20% higher in consistency in multi-turn user
studies) on the Persona-Chat dataset. Finally, we extensively analyze
Sketch-Fill-A-R's responses and human feedback, and show it is more consistent
and engaging by using more relevant responses and questions.
