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Register a new userEARL: Informative Knowledge-Grounded Conversation Generation with Entity-Agnostic Representation Learning
إيرل: توليد محادثة محادثة المعرفة المعرفة مع التعلم التمثيل اللاإرادي
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توليد الاستجابات الإعلامية والمناسبة صعبة ولكنها مهمة لبناء أنظمة الحوار يشبه الإنسان. على الرغم من أن نماذج المحادثة المختلفة المعرفة قد اقترحت، إلا أن هذه النماذج لها قيود في الاستفادة من المعرفة التي تحدث بشكل غير منتظم في بيانات التدريب، ناهيك عن دمج المعرفة غير المرئية في جيل المحادثة. في هذه الورقة، نقترح طريقة تعلم التمثيل المتعصب للكيان (EARL) لإدخال الرسوم البيانية المعرفة لتوليد المحادثة بالمعلومات. على عكس الأساليب التقليدية التي تقترب المعلمة التمثيل المحدد لكل كيان، فإن إيرل يستخدم سياق المحادثات والهيكل العلائقي لرسوم البيان البيئية لمعرفة تمثيل الفئة للكيانات، المعمم لإدماج كيانات غير مرئية في الرسوم البيانية المعرفة في جيل المحادثة. التقييمات التلقائية واليدوية توضح أن طرازنا يمكن أن يولد ردود أكثر إعلامية ومتماسكة وغير طبيعية من النماذج الأساسية.
Generating informative and appropriate responses is challenging but important for building human-like dialogue systems. Although various knowledge-grounded conversation models have been proposed, these models have limitations in utilizing knowledge that infrequently occurs in the training data, not to mention integrating unseen knowledge into conversation generation. In this paper, we propose an Entity-Agnostic Representation Learning (EARL) method to introduce knowledge graphs to informative conversation generation. Unlike traditional approaches that parameterize the specific representation for each entity, EARL utilizes the context of conversations and the relational structure of knowledge graphs to learn the category representation for entities, which is generalized to incorporating unseen entities in knowledge graphs into conversation generation. Automatic and manual evaluations demonstrate that our model can generate more informative, coherent, and natural responses than baseline models.
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