Can I read CDGP: Automatic Cloze Distractor Generation based on Pre-trained Language Model on EtoBox?

CDGP: Automatic Cloze Distractor Generation based on Pre-trained Language Model by Chiang, Shang-Hsuan; Wang, Ssu-Cheng; Fan, Yao-Chung is a scholarly article available to read on EtoBox.

What is CDGP: Automatic Cloze Distractor Generation based on Pre-trained Language Model about?

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.

Author

Chiang, Shang-Hsuan; Wang, Ssu-Cheng; Fan, Yao-Chung

Published

2024

Language

EN