NUANCED : Natural Utterance Annotation for Nuanced Conversation with Estimated Distributions (2021)

Abstract: 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 user-centric 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 code and data are publicly available.

## introduction conversational artificial intelligence is one of the long-standing research problems in natural language processing, such as task-oriented dialogue @xcite @xcite , conversational recommendation @xcite and chi-chat @xcite etc. however, most existing systems are agent-centric. such systems require the users to unnaturally adapt to and even have a learning curve on the system ontology, which is largely unknown system ontology: category: japanese, korean, chinese, new american, etc. alcohol: full bar, beer and wine, don't serve attire: casual, dressy, formal wifi: free, paid, no full bar please. slot: @xmath0, any preference on alcoholic beverages? ## related work task-oriented dialogue systems are typically divided into several sub modules, including user intent detection @xcite , dialogue state tracking @xcite , dialogue policy learning @xcite , and response generation @xcite . more recent approaches begin to build unified models that bring the pipeline together @xcite . conversational recommendation focus on combining the recommendation system with online conversation to capture user preference @xcite @xcite . previous works mostly focus on learning the agent side policy to ask the right questions and make accurate recommendations, such as @xcite @xcite . chit-chat @xcite is the most free form dialogue but almost with no knowledge grounding or state tracking. both existing task-oriented, conversational recommendation systems have a pre-defined system ontology as a representation connected to the back-end database. the ontology defines all entity attributes as slots and the option values for each slot. in existing datasets, such as the dstc challenges @xcite , multi-woz @xcite , mgconvrex @xcite , etc, the utterances from the users mostly closely follow the system ontology. while in task-oriented dialogue systems, parsing the user utterances into dialogue states is more on hard matching, in conversational recommendation systems soft matching is more encouraged since the user preferences are more salient and diverse in this type of conversations. ## experiments in this section, we conduct experiments on both versions of the datasets in §4.1 and §4.2, respectively. ## conclusion and open problems starting from our dataset, we believe the usercentric dialogue system is an open-ended problem and the following directions are worth pursuing: