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Generating diverse and relevant questions over text is a task with widespreadapplications. We argue that commonly-used evaluation metrics such as BLEU andMETEOR are not suitable for this task due to the inherent diversity ofreference questions, and propose a scheme for extending conventional metrics toreflect diversity. We furthermore propose a variational encoder-decoder modelfor this task. We show through automatic and human evaluation that ourvariational model improves diversity without loss of quality, and demonstratehow our evaluation scheme reflects this improvement.

Evaluating for Diversity in Question Generation over Text