  Deep learning models are often not easily adaptable to new tasks and require
task-specific adjustments. The differentiable neural computer (DNC), a
memory-augmented neural network, is designed as a general problem solver which
can be used in a wide range of tasks. But in reality, it is hard to apply this
model to new tasks. We analyze the DNC and identify possible improvements
within the application of question answering. This motivates a more robust and
scalable DNC (rsDNC). The objective precondition is to keep the general
character of this model intact while making its application more reliable and
speeding up its required training time. The rsDNC is distinguished by a more
robust training, a slim memory unit and a bidirectional architecture. We not
only achieve new state-of-the-art performance on the bAbI task, but also
minimize the performance variance between different initializations.
Furthermore, we demonstrate the simplified applicability of the rsDNC to new
tasks with passable results on the CNN RC task without adaptions.
