  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-the-art result from 14.94 to 34.17 (NDCG@10 score). Our
code and dataset is available at https://github.com/AndyChiangSH/CDGP.
