Extractive Question Answering Using Match LSTM and Answer Pointer
One of the major goals of Natural Language Processing is understanding text, and, in particular, reading comprehension (RC) the ability to read a passage and answer a question about it. The recently released Stanford Question Answering Dataset (SQuAD) is one of the first robust RC datasets and introduces the complexity of variable length and open ended rather than multiple choice answers. Here, we replicate the match LSTM with Pointer Net model introduced by Wang and Jiang (2016) and apply their boundary model to directly predict the start and end indices of the answer. Our model shows proof of concept and achieves basic functionality but falls short of the original authors' results. We analyze our results and suggest future model modifications to help achieve or surpass theirs.