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Register a new userCR-Walker: Tree-Structured Graph Reasoning and Dialog Acts for Conversational Recommendation
CR-Walker: تفكير الرسم البياني منظم في الأشجار وتطبيق الحوار لتوصية المحادثة
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تم إحراك المصالح المتزايدة في أنظمة الموافقة على المحادثة (CRS)، والتي تستكشف تفضيل المستخدم من خلال تفاعلات المحادثة من أجل تقديم توصية مناسبة. ومع ذلك، لا يزال هناك نقص في القدرة في CRS الحالية إلى (1) اجتياز مسارات التفكير المتعددة على المعرفة الأساسية لإدخال العناصر والسمات ذات الصلة، و (2) ترتيب كيانات مختارة بشكل مناسب بموجب نود النظام الحالي للسيطرة على جيل الاستجابة. لمعالجة هذه المشكلات، نقترح Walker CR-Walker في هذه الورقة، وهو نموذج يقوم بتنفيذ التفكير منظم في الأشجار في رسم بياني للمعرفة، ويولد أعمال حوار إعلامية لتوجيه توليد اللغة. ينظر المخطط الفريد من المنطق المنظم في الأشجار إلى الكيان اجتاز كل قفزة كجزء من أعمال الحوار لتسهيل توليد اللغة، والذي يربط كيف يتم اختيار الكيانات والأعرب عنها. تظهر التقييمات التلقائية والبشرية أن CR-Walker يمكن أن يصل إلى توصية أكثر دقة، وتوليد استجابات أكثر إعلامية وجذابة.
Growing interests have been attracted in Conversational Recommender Systems (CRS), which explore user preference through conversational interactions in order to make appropriate recommendation. However, there is still a lack of ability in existing CRS to (1) traverse multiple reasoning paths over background knowledge to introduce relevant items and attributes, and (2) arrange selected entities appropriately under current system intents to control response generation. To address these issues, we propose CR-Walker in this paper, a model that performs tree-structured reasoning on a knowledge graph, and generates informative dialog acts to guide language generation. The unique scheme of tree-structured reasoning views the traversed entity at each hop as part of dialog acts to facilitate language generation, which links how entities are selected and expressed. Automatic and human evaluations show that CR-Walker can arrive at more accurate recommendation, and generate more informative and engaging responses.
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