2024-06-24

【學術亮點-頂級會議論文】基於 Pre-trained 語言模型之自動選項生成研究

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【學術亮點-頂級會議論文】基於 Pre-trained 語言模型之自動選項生成研究
AI core Technology: Advanced Research and Resource Integration Platform or AI Technology
Department of Computer Science and Engineering / Yao-Chung Fan / Associate Professor

核心技術:AI核心技術之進階研究與資源整合平台【資訊工程學系范耀中副教授】
 
論文篇名 英文:CDGP: Automatic Cloze Distractor Generation based on Pre-trained Language Model
中文:基於 Pre-trained 語言模型之自動選項生成研究
期刊名稱 Findings of the Association for Computational Linguistics: EMNLP 2022 (指標清單期刊)
發表年份, 卷數, 起迄頁數 EMNLP 2022 , 2022, pp.5835-5840.
International Conference on Empirical Methods in Natural Language Processing (EMNLP 2022) in Abu Dhabi, United Arab Emirates from December 7th to 11th, 2022
作者 Shang-Hsuan Chiang; Ssu-Cheng Wang; Yao-Chung Fan(范耀中)
DOI 10.18653/v1/2022.findings-emnlp.429
英文摘要 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 this https URL.
上架日期:2022/12/7-12/11
 
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