论文标题
为多项选择问题生成足够的干扰物
Generating Adequate Distractors for Multiple-Choice Questions
论文作者
论文摘要
本文提出了一种新型的方法，可以自动生成从给定文章产生的给定问题对（QAP）的足够的分散分心，以形成足够的多项选择问题（MCQ）。我们的方法是词性标记，命名 - 实现标签，语义角度标记，正则表达式，域知识库，单词嵌入，单词编辑距离，WordNet和其他算法的组合。我们使用美国SAT（学术评估测试）实践阅读测试作为数据集，以生成QAP并为每个QAP生成三个干扰器以形成MCQ。我们表明，通过人工法官的实验和评估，每个MCQ至少具有一个足够的干扰因素，而84％的MCQ具有三个适当的干扰因素。
This paper presents a novel approach to automatic generation of adequate distractors for a given question-answer pair (QAP) generated from a given article to form an adequate multiple-choice question (MCQ). Our method is a combination of part-of-speech tagging, named-entity tagging, semantic-role labeling, regular expressions, domain knowledge bases, word embeddings, word edit distance, WordNet, and other algorithms. We use the US SAT (Scholastic Assessment Test) practice reading tests as a dataset to produce QAPs and generate three distractors for each QAP to form an MCQ. We show that, via experiments and evaluations by human judges, each MCQ has at least one adequate distractor and 84\% of MCQs have three adequate distractors.
