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Memory Networks have emerged as effective models to incorporate KnowledgeBases (KB) into neural networks. By storing KB embeddings into a memorycomponent, these models can learn meaningful representations that are groundedto external knowledge. However, as the memory unit becomes full, the oldestmemories are replaced by newer representations.  In this paper, we question this approach and provide experimental evidencethat conventional Memory Networks store highly correlated vectors duringtraining. While increasing the memory size mitigates this problem, this alsoleads to overfitting as the memory stores a large number of training latentrepresentations. To address these issues, we propose a novel regularizationmechanism named memory dropout which 1) Samples a single latent vector from thedistribution of redundant memories. 2) Ages redundant memories thus increasingtheir probability of overwriting them during training. This fullydifferentiable technique allows us to achieve state-of-the-art responsegeneration in the Stanford Multi-Turn Dialogue and Cambridge Restaurantdatasets.

Aging Memories Generate More Fluent Dialogue Responses with Memory Augmented Neural Networks