CDGP: Automatic Cloze Distractor Generation based on Pre-trained Language Model
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866917614711734272 |
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| author | Chiang, Shang-Hsuan Wang, Ssu-Cheng Fan, Yao-Chung |
| author_facet | Chiang, Shang-Hsuan Wang, Ssu-Cheng Fan, Yao-Chung |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_10326 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | CDGP: Automatic Cloze Distractor Generation based on Pre-trained Language Model Chiang, Shang-Hsuan Wang, Ssu-Cheng Fan, Yao-Chung Computation and Language Artificial Intelligence Machine Learning 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. |
| title | CDGP: Automatic Cloze Distractor Generation based on Pre-trained Language Model |
| topic | Computation and Language Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2403.10326 |