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.

提出了一个基于匹配式 LSTM 和 Pointer Net 的神经网络结构，用于机器理解文本，能够在 Stanford Question Answering Dataset 数据集上对传统的逻辑回归与手工特征提取方法结果有显著的提升。

使用 Match-LSTM 和答案指针 的机器阅读理解

Machine Comprehension Using Match-LSTM and Answer Pointer

We introduce a new neural architecture to learn the conditional probability
of an output sequence with elements that are discrete tokens corresponding to
positions in an input sequence. Such problems cannot be trivially addressed by
existent approaches such as sequence-to-sequence and Neural Turing Machines,
because the number of target classes in each step of the output depends on the
length of the input, which is variable. Problems such as sorting variable sized
sequences, and various combinatorial optimization problems belong to this
class. Our model solves the problem of variable size output dictionaries using
a recently proposed mechanism of neural attention. It differs from the previous
attention attempts in that, instead of using attention to blend hidden units of
an encoder to a context vector at each decoder step, it uses attention as a
pointer to select a member of the input sequence as the output. We call this
architecture a Pointer Net (Ptr-Net). We show Ptr-Nets can be used to learn
approximate solutions to three challenging geometric problems -- finding planar
convex hulls, computing Delaunay triangulations, and the planar Travelling
Salesman Problem -- using training examples alone. Ptr-Nets not only improve
over sequence-to-sequence with input attention, but also allow us to generalize
to variable size output dictionaries. We show that the learnt models generalize
beyond the maximum lengths they were trained on. We hope our results on these
tasks will encourage a broader exploration of neural learning for discrete
problems.

介绍一种新的神经网络结构用于学习输出序列的条件概率，能够解决存在变量数量目标类的问题，其中应用到了神经注意力机制，被称作指针网络，能够用于学习一些具有挑战性的几何问题。