  In recent years, memory-augmented neural networks(MANNs) have shown promising
power to enhance the memory ability of neural networks for sequential
processing tasks. However, previous MANNs suffer from complex memory addressing
mechanism, making them relatively hard to train and causing computational
overheads. Moreover, many of them reuse the classical RNN structure such as
LSTM for memory processing, causing inefficient exploitations of memory
information. In this paper, we introduce a novel MANN, the Auto-addressing and
Recurrent Memory Integrating Network (ARMIN) to address these issues. The ARMIN
only utilizes hidden state ht for automatic memory addressing, and uses a novel
RNN cell for refined integration of memory information. Empirical results on a
variety of experiments demonstrate that the ARMIN is more light-weight and
efficient compared to existing memory networks. Moreover, we demonstrate that
the ARMIN can achieve much lower computational overhead than vanilla LSTM while
keeping similar performances. Codes are available on github.com/zoharli/armin.
