  We present an approach for generating clarification questions with the goal
of eliciting new information that would make the given textual context more
complete. We propose that modeling hypothetical answers (to clarification
questions) as latent variables can guide our approach into generating more
useful clarification questions. We develop a Generative Adversarial Network
(GAN) where the generator is a sequence-to-sequence model and the discriminator
is a utility function that models the value of updating the context with the
answer to the clarification question. We evaluate on two datasets, using both
automatic metrics and human judgments of usefulness, specificity and relevance,
showing that our approach outperforms both a retrieval-based model and
ablations that exclude the utility model and the adversarial training.
