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.

Dataset Files

You must be an IEEE Dataport Subscriber to access these files. Subscribe now or login.

File readme2.14 KB
[1] Philip Huang, "EQGG-RACE", IEEE Dataport, 2022. [Online]. Available: Accessed: May. 18, 2022.
doi = {10.21227/x121-tk72},
url = {},
author = {Philip Huang },
publisher = {IEEE Dataport},
title = {EQGG-RACE},
year = {2022} }
AU - Philip Huang
PY - 2022
PB - IEEE Dataport
UR - 10.21227/x121-tk72
ER -
Philip Huang. (2022). EQGG-RACE. IEEE Dataport.
Philip Huang, 2022. EQGG-RACE. Available at:
Philip Huang. (2022). "EQGG-RACE." Web.
1. Philip Huang. EQGG-RACE [Internet]. IEEE Dataport; 2022. Available from :
Philip Huang. "EQGG-RACE." doi: 10.21227/x121-tk72