用Transformer和知识图谱生成高质量波斯语选择题
FarsiMCQGen: a Persian Multiple-choice Question Generation Framework
- 结合生成、过滤与排序，利用Transformer和知识图谱设计可信干扰项
- 构建10289条波斯语多选题数据集，经多个大模型验证质量优异
- 为低资源语言教育评估提供可复用的生成框架与数据集
多选题（MCQ）在教育测评中广泛应用，因其能高效评估学习者知识水平。然而，在低资源语言如波斯语中生成高质量多选题仍面临挑战。本文提出FarsiMCQGen，一种生成波斯语多选题的新方法。该方法融合候选题生成、筛选与排序技术，利用Transformer与知识图谱结合规则策略，构建出贴近真实题目的答案选项。研究基于维基百科数据，涵盖通用知识类题目。此外，本文构建了一个包含10,289道题的新型波斯语多选题数据集，并由多个前沿大语言模型进行评估。实验结果表明，该模型生成效果良好，数据集质量高，具有推动后续多选题研究的潜力。
原文摘要 · Abstract (English)
Multiple-choice questions (MCQs) are commonly used in educational testing, as they offer an efficient means of evaluating learners' knowledge. However, generating high-quality MCQs, particularly in low-resource languages such as Persian, remains a significant challenge. This paper introduces FarsiMCQGen, an innovative approach for generating Persian-language MCQs. Our methodology combines candidate generation, filtering, and ranking techniques to build a model that generates answer choices resembling those in real MCQs. We leverage advanced methods, including Transformers and knowledge graphs, integrated with rule-based approaches to craft credible distractors that challenge test-takers. Our work is based on data from Wikipedia, which includes general knowledge questions. Furthermore, this study introduces a novel Persian MCQ dataset comprising 10,289 questions. This dataset is evaluated by different state-of-the-art large language models (LLMs). Our results demonstrate the effectiveness of our model and the quality of the generated dataset, which has the potential to inspire further research on MCQs.
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