  Question Answering is a task which requires building models capable of
providing answers to questions expressed in human language. Full question
answering involves some form of reasoning ability. We introduce a neural
network architecture for this task, which is a form of $Memory\ Network$, that
recognizes entities and their relations to answers through a focus attention
mechanism. Our model is named $Question\ Dependent\ Recurrent\ Entity\ Network$
and extends $Recurrent\ Entity\ Network$ by exploiting aspects of the question
during the memorization process. We validate the model on both synthetic and
real datasets: the $bAbI$ question answering dataset and the $CNN\ \&\ Daily\
News$ $reading\ comprehension$ dataset. In our experiments, the models achieved
a State-of-The-Art in the former and competitive results in the latter.
