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Register a new userAugmenting Transformers with KNN-Based Composite Memory for Dialog
زيادة المحولات مع الذاكرة المركبة القائمة على KNN للحوار
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يمكن أن تستفيد مهام التعلم المختلفة من الوصول إلى معلومات خارجية عن طرائق مختلفة، مثل النص والصور.ركز العمل الحديث على تعلم الهندسة مع ذكريات كبيرة قادرة على تخزين هذه المعرفة.نقترحنا زيادة شبكات عصبية محول التوليد مع وحدات جلب المعلومات المستندة إلى KNN (KIF).تعلم كل وحدة KIF عملية قراءة للوصول إلى المعرفة الخارجية الثابتة.نحن نطبق هذه الوحدات النمطية لنمذجة الحوار المولاد، وهي مهمة صعبة حيث يجب استرجاع المعلومات بشكل مرني وإدماجها للحفاظ على موضوع وتدفق المحادثة.نوضح فعالية نهجنا من خلال تحديد المعرفة ذات الصلة المطلوبة للحوار دراية ولكن المشاركة من Wikipedia، والصور، وإظهار أن الاستفادة من هذه المعلومات المستردة تعمل على تحسين أداء النموذج، ويتم قياسه بواسطة التقييم التلقائي والإنساني.
Various machine learning tasks can benefit from access to external information of different modalities, such as text and images. Recent work has focused on learning architectures with large memories capable of storing this knowledge. We propose augmenting generative Transformer neural networks with KNN-based Information Fetching (KIF) modules. Each KIF module learns a read operation to access fixed external knowledge. We apply these modules to generative dialog modeling, a challenging task where information must be flexibly retrieved and incorporated to maintain the topic and flow of conversation. We demonstrate the effectiveness of our approach by identifying relevant knowledge required for knowledgeable but engaging dialog from Wikipedia, images, and human-written dialog utterances, and show that leveraging this retrieved information improves model performance, measured by automatic and human evaluation.
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