Answering SQuAD

Question Answering is a very important cornerstone of natural language processing where a machine should be able to understand human language to an extent that an individual could ask questions to the machine in the language he/she understands and then get the answers back in the same language without the involvement of experts. In this project we evaluate a neural network based Question Answering system using a simple model and see how a very basic model performs on the Stanford Question Answering dataset.