CDGP: Automatic Cloze Distractor Generation based on Pre-trained Language Model
<|reference_start|>CDGP: Automatic Cloze Distractor Generation based on Pre-trained Language Model: 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.<|reference_end|>
@article{chiang2024cdgp:,
    title={CDGP: Automatic Cloze Distractor Generation based on Pre-trained
  Language Model},
    author={Shang-Hsuan Chiang and Ssu-Cheng Wang and Yao-Chung Fan},
    journal={chiang-etal-2022-cdgp},
    year={2024},
    doi={10.18653/v1/2022.findings-emnlp.429},
    archivePrefix={arXiv},
    eprint={2403.10326},
    primaryClass={cs.CL cs.AI cs.LG}
}