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.- Anthology ID:
- 2022.findings-emnlp.429
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2022
- Month:
- December
- Year:
- 2022
- Address:
- Abu Dhabi, United Arab Emirates
- Editors:
- Yoav Goldberg, Zornitsa Kozareva, Yue Zhang
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 5835–5840
- Language:
- URL:
- https://aclanthology.org/2022.findings-emnlp.429
- DOI:
- 10.18653/v1/2022.findings-emnlp.429
- Cite (ACL):
- Shang-Hsuan Chiang, Ssu-Cheng Wang, and Yao-Chung Fan. 2022. CDGP: Automatic Cloze Distractor Generation based on Pre-trained Language Model. In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 5835–5840, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
- Cite (Informal):
- CDGP: Automatic Cloze Distractor Generation based on Pre-trained Language Model (Chiang et al., Findings 2022)
- PDF:
- https://preview.aclanthology.org/naacl24-info/2022.findings-emnlp.429.pdf