  The last several years have seen intensive interest in exploring
neural-network-based models for machine comprehension (MC) and question
answering (QA). In this paper, we approach the problems by closely modelling
questions in a neural network framework. We first introduce syntactic
information to help encode questions. We then view and model different types of
questions and the information shared among them as an adaptation task and
proposed adaptation models for them. On the Stanford Question Answering Dataset
(SQuAD), we show that these approaches can help attain better results over a
competitive baseline.
