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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[1] Philip Huang, "EQGG-RACE", IEEE Dataport, 2022. [Online]. Available: http://dx.doi.org/10.21227/x121-tk72. Accessed: May. 18, 2022.
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doi = {10.21227/x121-tk72},
url = {http://dx.doi.org/10.21227/x121-tk72},
author = {Philip Huang },
publisher = {IEEE Dataport},
title = {EQGG-RACE},
year = {2022} }
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Philip Huang. (2022). EQGG-RACE. IEEE Dataport. http://dx.doi.org/10.21227/x121-tk72
Philip Huang, 2022. EQGG-RACE. Available at: http://dx.doi.org/10.21227/x121-tk72.
Philip Huang. (2022). "EQGG-RACE." Web.
1. Philip Huang. EQGG-RACE [Internet]. IEEE Dataport; 2022. Available from : http://dx.doi.org/10.21227/x121-tk72
Philip Huang. "EQGG-RACE." doi: 10.21227/x121-tk72