Attention-based Recurrent Neural Networks for Question Answering

Machine Comprehension (MC) of text is an important problem in Natural Language Processing (NLP) research, and the task of Question Answering (QA) is a major way of assessing MC outcomes. One QA dataset that has gained immense popularity recently is the Stanford Question Answering Dataset (SQuAD). Successful models for SQuAD have all involved the use of Recurrent Neural Network (RNN), and most of them apply the attention mechanism on top of the neural architecture. In this paper, we explore and compare two of such models Match-LSTM and Bidirectional Attention Flow (BiDAF), and propose an ensemble model which combines these two models together. Our models are able to achieve competitive results on the CS224N Test Leaderboard.