An LSTM Attention-based Network for Reading Comprehension

A significant goal of natural language processing (NLP) is to devise a system capable of machine understanding of text. A typical system can be tested on its ability to answer questions based on a given context document. One appropriate dataset for such a system is the Stanford Question Answering Dataset (SQuAD), a crowdsourced dataset of over 100k (question, context, answer) triplets. In this work, we focused on creating such a question answering system through a neural net architecture modeled after the attentive reader and sequence attention mix models.