NUANCED: Natural Utterance Annotation for Nuanced Conversation with Estimated Distributions
Zhiyu Chen, Honglei Liu, Hu Xu, Seungwhan Moon, Hao Zhou, Bing Liu · 2021
Existing conversational systems are mostly agent-centric, which assumes the user utterances will closely follow the system ontology.However, in real-world scenarios, it is highly desirable that users can speak freely and naturally.In this work, we attempt to build a usercentric dialogue system for conversational recommendation.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 user's utterances to such distributions.Learning such a mapping poses new challenges on reasoning over various types of 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, with 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 dataset is publicly available 1 .