  Recent work has shown that memory modules are crucial for the generalization
ability of neural networks on learning simple algorithms. However, we still
have little understanding of the working mechanism of memory modules. To
alleviate this problem, we apply a two-step analysis pipeline consisting of
first inferring hypothesis about what strategy the model has learned according
to visualization and then verify it by a novel proposed qualitative analysis
method based on dimension reduction. Using this method, we have analyzed two
popular memory-augmented neural networks, neural Turing machine and
stack-augmented neural network on two simple algorithm tasks including
reversing a random sequence and evaluation of arithmetic expressions. Results
have shown that on the former task both models can learn to generalize and on
the latter task only the stack-augmented model can do so. We show that
different strategies are learned by the models, in which specific categories of
input are monitored and different policies are made based on that to change the
memory.

Machine Learning;Computation and Language;Neural and Evolutionary Computing