@inproceedings{yu-etal-2024-enhancing,
title = "Enhancing Distractor Generation for Multiple-Choice Questions with Retrieval Augmented Pretraining and Knowledge Graph Integration",
author = "Yu, Han Cheng and
Shih, Yu An and
Law, Kin Man and
Hsieh, KaiYu and
Cheng, Yu Chen and
Ho, Hsin Chih and
Lin, Zih An and
Hsu, Wen-Chuan and
Fan, Yao-Chung",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Findings of the Association for Computational Linguistics ACL 2024",
month = aug,
year = "2024",
address = "Bangkok, Thailand and virtual meeting",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.findings-acl.655",
pages = "11019--11029",
abstract = "In this paper, we tackle the task of distractor generation (DG) for multiple-choice questions. Our study introduces two key designs. First, we propose the concept of retrieval augmented pretraining, which involves refining the language model pretraining to align it more closely with the downstream task of DG. Second, we explore the integration of knowledge graphs and language models to further enhance the performance of DG. Our study unveils promising directions for further development in DG by showcasing the efficacy of knowledge augmentation and task-specific pretraining. These findings demonstrate the potential for leveraging both strategies to enhance the quality and performance of DG systems.",
}
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%0 Conference Proceedings
%T Enhancing Distractor Generation for Multiple-Choice Questions with Retrieval Augmented Pretraining and Knowledge Graph Integration
%A Yu, Han Cheng
%A Shih, Yu An
%A Law, Kin Man
%A Hsieh, KaiYu
%A Cheng, Yu Chen
%A Ho, Hsin Chih
%A Lin, Zih An
%A Hsu, Wen-Chuan
%A Fan, Yao-Chung
%Y Ku, Lun-Wei
%Y Martins, Andre
%Y Srikumar, Vivek
%S Findings of the Association for Computational Linguistics ACL 2024
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand and virtual meeting
%F yu-etal-2024-enhancing
%X In this paper, we tackle the task of distractor generation (DG) for multiple-choice questions. Our study introduces two key designs. First, we propose the concept of retrieval augmented pretraining, which involves refining the language model pretraining to align it more closely with the downstream task of DG. Second, we explore the integration of knowledge graphs and language models to further enhance the performance of DG. Our study unveils promising directions for further development in DG by showcasing the efficacy of knowledge augmentation and task-specific pretraining. These findings demonstrate the potential for leveraging both strategies to enhance the quality and performance of DG systems.
%U https://aclanthology.org/2024.findings-acl.655
%P 11019-11029
Markdown (Informal)
[Enhancing Distractor Generation for Multiple-Choice Questions with Retrieval Augmented Pretraining and Knowledge Graph Integration](https://aclanthology.org/2024.findings-acl.655) (Yu et al., Findings 2024)
ACL
- Han Cheng Yu, Yu An Shih, Kin Man Law, KaiYu Hsieh, Yu Chen Cheng, Hsin Chih Ho, Zih An Lin, Wen-Chuan Hsu, and Yao-Chung Fan. 2024. Enhancing Distractor Generation for Multiple-Choice Questions with Retrieval Augmented Pretraining and Knowledge Graph Integration. In Findings of the Association for Computational Linguistics ACL 2024, pages 11019–11029, Bangkok, Thailand and virtual meeting. Association for Computational Linguistics.