  Learning to remember long sequences remains a challenging task for recurrent
neural networks. Register memory and attention mechanisms were both proposed to
resolve the issue with either high computational cost to retain memory
differentiability, or by discounting the RNN representation learning towards
encoding shorter local contexts than encouraging long sequence encoding.
Associative memory, which studies the compression of multiple patterns in a
fixed size memory, were rarely considered in recent years. Although some recent
work tries to introduce associative memory in RNN and mimic the energy decay
process in Hopfield nets, it inherits the shortcoming of rule-based memory
updates, and the memory capacity is limited. This paper proposes a method to
learn the memory update rule jointly with task objective to improve memory
capacity for remembering long sequences. Also, we propose an architecture that
uses multiple such associative memory for more complex input encoding. We
observed some interesting facts when compared to other RNN architectures on
some well-studied sequence learning tasks.
