Papers by Yu An Shih
Enhancing Distractor Generation for Multiple-Choice Questions with Retrieval Augmented Pretraining and Knowledge Graph Integration (2024.findings-acl)
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Han Cheng Yu, Yu An Shih, Kin Man Law, KaiYu Hsieh, Yu Chen Cheng, Hsin Chih Ho, Zih An Lin, Wen-Chuan Hsu, Yao-Chung Fan
| Challenge: | Existing LMs undergo task-agnostic pertaining, but task-specific pretraining has gained prominence. |
| Approach: | They propose retrieval augmented pretraining and task-specific pretraining for DG . they propose to refine language model pretraining to align it more closely with downstream task . |
| Outcome: | The proposed method improves the performance of multiple-choice questions by integrating knowledge graphs and language models. |
Distractor Generation based on Text2Text Language Models with Pseudo Kullback-Leibler Divergence Regulation (2023.findings-acl)
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Hui-Juan Wang, Kai-Yu Hsieh, Han-Cheng Yu, Jui-Ching Tsou, Yu An Shih, Chen-Hua Huang, Yao-Chung Fan
| Challenge: | Existing methods for cloze-style multiple choice questions (MCQs) distractor generation are based on knowledge bases and pre-trained language models. |
| Approach: | They propose to formulate cloze distractor generation task as Text2Text task and propose a pseudo Kullback-Leibler divergence for regulating the generation to consider item discrimination index in education evaluation. |
| Outcome: | The proposed model improves state-of-the-art performance from 10.81 to 22.00 (p@1 score) |