  Recent open-domain dialogue models have brought numerous breakthroughs.
However, building a chat system is not scalable since it often requires a
considerable volume of human-human dialogue data, especially when enforcing
features such as persona, style, or safety. In this work, we study the
challenge of imposing roles on open-domain dialogue systems, with the goal of
making the systems maintain consistent roles while conversing naturally with
humans. To accomplish this, the system must satisfy a role specification that
includes certain conditions on the stated features as well as a system policy
on whether or not certain types of utterances are allowed. For this, we propose
an efficient data collection framework leveraging in-context few-shot learning
of large-scale language models for building role-satisfying dialogue dataset
from scratch. We then compare various architectures for open-domain dialogue
systems in terms of meeting role specifications while maintaining
conversational abilities. Automatic and human evaluations show that our models
return few out-of-bounds utterances, keeping competitive performance on general
metrics. We release a Korean dialogue dataset we built for further research.
