  The ability to generate clarification questions i.e., questions that identify
useful missing information in a given context, is important in reducing
ambiguity. Humans use previous experience with similar contexts to form a
global view and compare it to the given context to ascertain what is missing
and what is useful in the context. Inspired by this, we propose a model for
clarification question generation where we first identify what is missing by
taking a difference between the global and the local view and then train a
model to identify what is useful and generate a question about it. Our model
outperforms several baselines as judged by both automatic metrics and humans.
