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

Ask what's missing and what's useful: Improving Clarification Question Generation using Global Knowledge