  Despite recent progress in memory augmented neural network (MANN) research,
associative memory networks with a single external memory still show limited
performance on complex relational reasoning tasks. Especially the content-based
addressable memory networks often fail to encode input data into rich enough
representation for relational reasoning and this limits the relation modeling
performance of MANN for long temporal sequence data. To address these problems,
here we introduce a novel Distributed Associative Memory architecture (DAM)
with Memory Refreshing Loss (MRL) which enhances the relation reasoning
performance of MANN. Inspired by how the human brain works, our framework
encodes data with distributed representation across multiple memory blocks and
repeatedly refreshes the contents for enhanced memorization similar to the
rehearsal process of the brain. For this procedure, we replace a single
external memory with a set of multiple smaller associative memory blocks and
update these sub-memory blocks simultaneously and independently for the
distributed representation of input data. Moreover, we propose MRL which
assists a task's target objective while learning relational information
existing in data. MRL enables MANN to reinforce an association between input
data and task objective by reproducing stochastically sampled input data from
stored memory contents. With this procedure, MANN further enriches the stored
representations with relational information. In experiments, we apply our
approaches to Differential Neural Computer (DNC), which is one of the
representative content-based addressing memory models and achieves the
state-of-the-art performance on both memorization and relational reasoning
tasks.
