CDGP: Automatic Cloze Distractor Generation based on Pre-trained Language Model
Shang-Hsuan Chiang, Ssu-Cheng Wang, Yao-Chung Fan · 2022
Manually designing cloze test consumes enormous time and efforts.The major challenge lies in wrong option (distractor) selection.Having carefully-design distractors improves the effectiveness of learner ability assessment.As a result, the idea of automatically generating cloze distractor is motivated.In this paper, we investigate cloze distractor generation by exploring the employment of pre-trained language models (PLMs) as an alternative for candidate distractor generation.Experiments show that the PLM-enhanced model brings a substantial performance improvement.Our best performing model advances the state-of-theart result from 14.94 to 34.17 (NDCG@10 score).Our code and dataset is available at https://github.com/AndyChiangSH/CDGP.