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Register a new userKERS: A Knowledge-Enhanced Framework for Recommendation Dialog Systems with Multiple Subgoals
KERS: إطار محسن المعرفة لأنظمة حوار التوصية مع فروع متعددة
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تتطلب مربعات الحوار التوصية النظام لبناء رابطة اجتماعية مع المستخدمين للحصول على الثقة وتطوير تقارب من أجل زيادة فرصة توصية ناجحة. من المفيد تقسيم هذه المحادثات، مثل هذه المحادثات مع مجموعات متعددة (مثل الدردشة الاجتماعية، الإجابة على السؤال، والتوصية، وما إلى ذلك)، بحيث يمكن للنظام استرداد المعرفة المناسبة بدقة أفضل بموجب علمي مختلفين. في هذه الورقة، نقترح إطارا موحدا للحوار المشترك متعدد الهيئات المستندة إلى المعرفة: نظام التوصية المحسن المعزز للمعارف (KERS). نتنبأ أولا بتسلسل من الفئة الفرعية واستخدامها لتوجيه نموذج الحوار لتحديد المعرفة من مجموعة فرعية من الرسم البياني المعرفي الحالي. ثم نقترح ثلاث آليات جديدة لتصفية المعرفة الصاخبة وتعزيز إدراج المعرفة التي تنظيفها في عملية توليد استجابة الحوار. تظهر التجارب أن طريقتنا تحصل على نتائج حديثة على مجموعة بيانات Dreecdial في كل من التقييم التلقائي والبشري.
Recommendation dialogs require the system to build a social bond with users to gain trust and develop affinity in order to increase the chance of a successful recommendation. It is beneficial to divide up, such conversations with multiple subgoals (such as social chat, question answering, recommendation, etc.), so that the system can retrieve appropriate knowledge with better accuracy under different subgoals. In this paper, we propose a unified framework for common knowledge-based multi-subgoal dialog: knowledge-enhanced multi-subgoal driven recommender system (KERS). We first predict a sequence of subgoals and use them to guide the dialog model to select knowledge from a sub-set of existing knowledge graph. We then propose three new mechanisms to filter noisy knowledge and to enhance the inclusion of cleaned knowledge in the dialog response generation process. Experiments show that our method obtains state-of-the-art results on DuRecDial dataset in both automatic and human evaluation.
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