  Existing conversational systems are mostly agent-centric, which assumes the
user utterances would closely follow the system ontology (for NLU or dialogue
state tracking). However, in real-world scenarios, it is highly desirable that
the users can speak freely in their own way. It is extremely hard, if not
impossible, for the users to adapt to the unknown system ontology. In this
work, we attempt to build a user-centric dialogue system. As there is no clean
mapping for a user's free form utterance to an ontology, we first model the
user preferences as estimated distributions over the system ontology and map
the users' utterances to such distributions. Learning such a mapping poses new
challenges on reasoning over existing knowledge, ranging from factoid
knowledge, commonsense knowledge to the users' own situations. To this end, we
build a new dataset named NUANCED that focuses on such realistic settings for
conversational recommendation. Collected via dialogue simulation and
paraphrasing, NUANCED contains 5.1k dialogues, 26k turns of high-quality user
responses. We conduct experiments, showing both the usefulness and challenges
of our problem setting. We believe NUANCED can serve as a valuable resource to
push existing research from the agent-centric system to the user-centric
system. The code and data is publicly available at
\url{https://github.com/facebookresearch/nuanced}.
