This paper investigates the issue of generating multiple questions with respect to a given context paragraph. Existing designs of question generation (QG) model take no notice of intra-group similarity and type diversity for forming a question group. These attributes are critical for employing QG techniques in educational applications. This paper proposes a two-stage framework by combining neural language models and genetic algorithm for the question group generation task.

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