CDGP: Automatic Cloze Distractor Generation based on Pre-trained Language Model

Shang-Hsuan Chiang, Ssu-Cheng Wang, Yao-Chung Fan
3/15/2024
cs.CLcs.AIcs.LG

Official code: AndyChiangSH/CDGP (12 stars, MIT, last commit 2023-05-22).

Abstract

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.

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Code Implementations(1)

AndyChiangSH/CDGPOfficial100%
MIT

Code for Findings of EMNLP 2022 short paper "CDGP: Automatic Cloze Distractor Generation based on Pre-trained Language Model".

124Python, Jupyter NotebookMar 12, 20223 years agoMIT
bartbertcloze-questionsdistractor-generationfesttext+7 more

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Cite this paper

@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},
  year   = {2024},
  eprint = {2403.10326},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2403.10326v1}
}

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