Machine Comprehension
Machine Comprehension is a fundamental problem in Natural Language. A recently released dataset, the Stanford Question Answering Dataset (SQuAD) offers a large number of real questions and their answers created by humans through crowdsourcing. SQuAD provides a challenging testbed for evaluating machine comprehension algorithms, partly because compared with previous datasets, in SQuAD the answers do not come from a small set of candidate answers and they have variable lengths. In this project we implemented two models First we implemented Match LSTM model using answer pointer using boundary model. Then we also implemented another model based on Scaled Dot Product Attention and Multi Head Attention. The later model gives better results.