Ensemble Learning For Machine Comprehension Bidirectional Attention Flow Models
In this paper, we will explore machine comprehension in Stanford Question and Answering Dataset using ensembled deep recurrent neural networks with bi directional attention flow. Given a context paragraph, we attempt to answer a query related to the context paragraph. This requires use to not only generate knowledge representation for each question and paragraph, but also create mechanisms that explore attention between the questions and paragraphs. In this paper, we use bi directional attention flow networks that use bi directional long short term memory recurrent neural networks to help represent the context, the questions and their interactions at multiple levels of granularity. Our best ensemble model achieves 63.748 F1 and 52.507 EM scores on the development set and 64.41 F1 and 53.498 EM scores on the test set, all published on CodaLab.