  Machine comprehension of text is an important problem in natural language
processing. A recently released dataset, the Stanford Question Answering
Dataset (SQuAD), offers a large number of real questions and their answers
created by humans through crowdsourcing. SQuAD provides a challenging testbed
for evaluating machine comprehension algorithms, partly because compared with
previous datasets, in SQuAD the answers do not come from a small set of
candidate answers and they have variable lengths. We propose an end-to-end
neural architecture for the task. The architecture is based on match-LSTM, a
model we proposed previously for textual entailment, and Pointer Net, a
sequence-to-sequence model proposed by Vinyals et al.(2015) to constrain the
output tokens to be from the input sequences. We propose two ways of using
Pointer Net for our task. Our experiments show that both of our two models
substantially outperform the best results obtained by Rajpurkar et al.(2016)
using logistic regression and manually crafted features.
