Augmenting Transformers with KNN-Based Composite Memory for Dialogue

Understand

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.