Understanding unstructured text is a major goal within natural language
processing. Comprehension tests pose questions based on short text passages to
evaluate such understanding. In this work, we investigate machine comprehension
on the challenging {\it MCTest} benchmark. Partly because of its limited size,
prior work on {\it MCTest} has focused mainly on engineering better features.
We tackle the dataset with a neural approach, harnessing simple neural networks
arranged in a parallel hierarchy. The parallel hierarchy enables our model to
compare the passage, question, and answer from a variety of trainable
perspectives, as opposed to using a manually designed, rigid feature set.
Perspectives range from the word level to sentence fragments to sequences of
sentences; the networks operate only on word-embedding representations of text.
When trained with a methodology designed to help cope with limited training
data, our Parallel-Hierarchical model sets a new state of the art for {\it
MCTest}, outperforming previous feature-engineered approaches slightly and
previous neural approaches by a significant margin (over 15\% absolute).

本篇论文中，我们基于神经网络提出了一种新的方法 Parallel-Hierarchical model 来应对训练数据量有限的 MCTest 数据集中自然语言理解问题，并在评测中取得了新的 state of the art，表现优于之前的手工特征提取方法和神经网络方法。