  Understanding open-domain text is one of the primary challenges in natural
language processing (NLP). Machine comprehension benchmarks evaluate the
system's ability to understand text based on the text content only. In this
work, we investigate machine comprehension on MCTest, a question answering (QA)
benchmark. Prior work is mainly based on feature engineering approaches. We
come up with a neural network framework, named hierarchical attention-based
convolutional neural network (HABCNN), to address this task without any
manually designed features. Specifically, we explore HABCNN for this task by
two routes, one is through traditional joint modeling of passage, question and
answer, one is through textual entailment. HABCNN employs an attention
mechanism to detect key phrases, key sentences and key snippets that are
relevant to answering the question. Experiments show that HABCNN outperforms
prior deep learning approaches by a big margin.
