A new model for Machine Comprehension via multi-perspective context matching and bidrectional attention flow

To answer a question about a context paragraph, there needs to be a complex model for interactions between these two. Previous Machine Comprehension (MC) where either not large enough to train end-to-end deep neural networks, or not hard to learn. Recently, after the release of SQuAD dataset dataset, several adept models have been proposed for the task of MC. In this work we try to combine the ideas of two state-of-the-art models (BiDAF and MPCM) with our new ideas to obtain a new model for question answering task. Promising experimental results on the test set of SQuAD encourages us to continue working on the proposed model.