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arXiv · 2403.10326

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

Shang-Hsuan Chiang·Ssu-Cheng Wang·Yao-Chung Fan
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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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Shang-Hsuan Chiang, Ssu-Cheng Wang, Yao-Chung Fan. 2024-03-15. CDGP: Automatic Cloze Distractor Generation based on Pre-trained Language Model. https://doi.org/10.18653/v1/2022.findings-emnlp.429

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