Question Dependent Recurrent Entity Network for Question Answering

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 $MemoryNetwork$, that recognizes entities and their relations to answers through a focus attention mechanism. Our model is named $QuestionDependentRecurrentEntityNetwork$ and extends $RecurrentEntityNetwork$ 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 $CNN\ &\ Daily
News$ $readingcomprehension$ dataset. In our experiments, the models achieved a State-of-The-Art in the former and competitive results in the latter.