结合检索增强预训练与知识图谱融合的选择题干扰项生成增强方法
Enhancing Distractor Generation for Multiple-Choice Questions with Retrieval Augmented Pretraining and Knowledge Graph Integration
AI总结：
该研究针对选择题干扰项生成任务，提出检索增强预训练方法并融合知识图谱，在MCQ、Sciq基准数据集上的F1@3分数均优于现有最优水平，有效提升了任务性能。
摘要：
本文针对选择题干扰项生成（DG）任务展开研究，提出两项核心设计。其一为检索增强预训练，即优化语言模型的预训练过程，使其更贴合下游DG任务的需求；其二是探索知识图谱的融合方式，以提升DG任务的性能。在基准数据集上的实验结果表明，本文提出的模型性能显著优于现有最优结果：性能最优的模型将MCQ数据集的F1@3分数从14.80提升至16.47，将Sciq数据集的F1@3分数从15.92提升至16.50。
英文摘要：
In this paper, we tackle the task of distractor generation (DG) for multiple-choice questions. Our study introduces two key designs. First, we propose \textit{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 to enhance the performance of DG. Through experiments with benchmarking datasets, we show that our models significantly outperform the state-of-the-art results. Our best-performing model advances the F1@3 score from 14.80 to 16.47 in MCQ dataset and from 15.92 to 16.50 in Sciq dataset.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
