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Existing conversational systems are mostly agent-centric, which assumes theuser utterances would closely follow the system ontology (for NLU or dialoguestate tracking). However, in real-world scenarios, it is highly desirable thatthe users can speak freely in their own way. It is extremely hard, if notimpossible, for the users to adapt to the unknown system ontology. In thiswork, we attempt to build a user-centric dialogue system. As there is no cleanmapping for a user's free form utterance to an ontology, we first model theuser preferences as estimated distributions over the system ontology and mapthe users' utterances to such distributions. Learning such a mapping poses newchallenges on reasoning over existing knowledge, ranging from factoidknowledge, commonsense knowledge to the users' own situations. To this end, webuild a new dataset named NUANCED that focuses on such realistic settings forconversational recommendation. Collected via dialogue simulation andparaphrasing, NUANCED contains 5.1k dialogues, 26k turns of high-quality userresponses. We conduct experiments, showing both the usefulness and challengesof our problem setting. We believe NUANCED can serve as a valuable resource topush existing research from the agent-centric system to the user-centricsystem. The code and data is publicly available at\url{https://github.com/facebookresearch/nuanced}.

NUANCED: Natural Utterance Annotation for Nuanced Conversation with Estimated Distributions