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The Differentiable Neural Computer (DNC) can learn algorithmic and questionanswering tasks. An analysis of its internal activation patterns reveals threeproblems: Most importantly, the lack of key-value separation makes the addressdistribution resulting from content-based look-up noisy and flat, since thevalue influences the score calculation, although only the key should. Second,DNC's de-allocation of memory results in aliasing, which is a problem forcontent-based look-up. Thirdly, chaining memory reads with the temporal linkagematrix exponentially degrades the quality of the address distribution. Ourproposed fixes of these problems yield improved performance on arithmetictasks, and also improve the mean error rate on the bAbI question answeringdataset by 43%.

Improving Differentiable Neural Computers Through Memory Masking, De-allocation, and Link Distribution Sharpness Control