@inproceedings{chiang-etal-2022-cdgp,
title = "{CDGP}: Automatic Cloze Distractor Generation based on Pre-trained Language Model",
author = "Chiang, Shang-Hsuan and
Wang, Ssu-Cheng and
Fan, Yao-Chung",
editor = "Goldberg, Yoav and
Kozareva, Zornitsa and
Zhang, Yue",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2022",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.findings-emnlp.429",
doi = "10.18653/v1/2022.findings-emnlp.429",
pages = "5835--5840",
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 \url{https://github.com/AndyChiangSH/CDGP}.",
}
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%0 Conference Proceedings
%T CDGP: Automatic Cloze Distractor Generation based on Pre-trained Language Model
%A Chiang, Shang-Hsuan
%A Wang, Ssu-Cheng
%A Fan, Yao-Chung
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Findings of the Association for Computational Linguistics: EMNLP 2022
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F chiang-etal-2022-cdgp
%X 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.
%R 10.18653/v1/2022.findings-emnlp.429
%U https://aclanthology.org/2022.findings-emnlp.429
%U https://doi.org/10.18653/v1/2022.findings-emnlp.429
%P 5835-5840
Markdown (Informal)
[CDGP: Automatic Cloze Distractor Generation based on Pre-trained Language Model](https://aclanthology.org/2022.findings-emnlp.429) (Chiang et al., Findings 2022)
ACL