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Learning to remember long sequences remains a challenging task for recurrentneural networks. Register memory and attention mechanisms were both proposed toresolve the issue with either high computational cost to retain memorydifferentiability, or by discounting the RNN representation learning towardsencoding shorter local contexts than encouraging long sequence encoding.Associative memory, which studies the compression of multiple patterns in afixed size memory, were rarely considered in recent years. Although some recentwork tries to introduce associative memory in RNN and mimic the energy decayprocess in Hopfield nets, it inherits the shortcoming of rule-based memoryupdates, and the memory capacity is limited. This paper proposes a method tolearn the memory update rule jointly with task objective to improve memorycapacity for remembering long sequences. Also, we propose an architecture thatuses multiple such associative memory for more complex input encoding. Weobserved some interesting facts when compared to other RNN architectures onsome well-studied sequence learning tasks.

Learning to update Auto-associative Memory in Recurrent Neural Networks for Improving Sequence Memorization