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Register a new userNUANCED: Natural Utterance Annotation for Nuanced Conversation with Estimated Distributions
إحكيم: التعليق التوضيحي الكلام الطبيعي للمحادثة ذات الدقة مع التوزيعات المقدرة
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غالبا ما تكون أنظمة المحادثة الموجودة في معظمها، مما يفترض أن تصطب المستخدمين سيتبعون عن كثب نظام ontology. ومع ذلك، في سيناريوهات العالم الواقعي، من المستحسن للغاية أن يستخدم المستخدمون التحدث بحرية وبطبيعة الحال. في هذا العمل، نحاول بناء نظام حوار تركز على المستخدمين لتوصية المحادثة. نظرا لعدم وجود رسم خرائط نظيفة لنكل النموذج المجاني للمستخدم لعلاج الأطباق، فإننا نقوم أولا بنموذج تفضيلات المستخدمين كتوزيعات مقدرة على نظام OnTology ونصوص المستخدمين على هذه التوزيعات. إن تعلم مثل هذه الرسوم الخرائط يشكل تحديات جديدة على التفكير في أنواع مختلفة من المعرفة، بدءا من المعرفة العفاهية، ومعرفة المنطقية لحالات المستخدمين الخاصة. تحقيقا لهذه الغاية، نبني مجموعة بيانات جديدة تسمى الدقيقة التي تركز على هذه الإعدادات الواقعية، مع حوارات 5.1k، تتحول 26 ألفا إلى ردود المستخدم عالية الجودة. نقوم بإجراء تجارب، مما يدل على حد سواء فائدة وتحديات إعداد مشكلتنا. نعتقد أن الدقة يمكن أن يكون بمثابة مورد قيمة لدفع الأبحاث الحالية من النظام المركزي للعميل إلى النظام المركزي للمستخدم. الرمز والبيانات متاح علنا.
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.
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https://aclanthology.org/
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