Summary of Cdgp: Automatic Cloze Distractor Generation Based on Pre-trained Language Model, by Shang-hsuan Chiang and Ssu-cheng Wang and Yao-chung Fan
CDGP: Automatic Cloze Distractor Generation based on Pre-trained Language Model
by Shang-Hsuan Chiang, Ssu-Cheng Wang, Yao-Chung Fan
First submitted to arxiv on: 15 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes an approach to automatically generate cloze test distractors using pre-trained language models (PLMs), aiming to improve the effectiveness of learner ability assessment. By leveraging PLMs, the authors demonstrate a substantial performance improvement in generating high-quality distractors, advancing the state-of-the-art result from 14.94 to 34.17 (NDCG@10 score). The proposed method employs PLMs as an alternative for candidate distractor generation and has the potential to reduce the time and effort required for manually designing cloze tests. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper explores using pre-trained language models (PLMs) to automatically generate cloze test distractors. This approach aims to make learner ability assessment more efficient and effective. By testing different methods, researchers found that PLMs can greatly improve the quality of generated distractors, making it a promising solution for reducing the time and effort needed to design cloze tests. |
