  Online reviews provide rich information about products and service, while it
remains inefficient for potential consumers to exploit the reviews for
fulfilling their specific information need. We propose to explore question
generation as a new way of exploiting review information. One major challenge
of this task is the lack of review-question pairs for training a neural
generation model. We propose an iterative learning framework for handling this
challenge via adaptive transfer and augmentation of the training instances with
the help of the available user-posed question-answer data. To capture the
aspect characteristics in reviews, the augmentation and generation procedures
incorporate related features extracted via unsupervised learning. Experiments
on data from 10 categories of a popular E-commerce site demonstrate the
effectiveness of the framework, as well as the usefulness of the new task.
