Question dependent recurrent entity network for question answering

Abstract

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 De-pendent 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 com-prehension dataset. In our experiments, the models achieved a State-of-The-Art in the former and competitive results in the latter.