用神经网络生成填空题，关键在构造多词干扰项。
Constructing Cloze Questions Generatively
- 基于语义消歧与词网构建干扰项生成框架
- 生成的干扰项在人类评估中质量高，超越现有最佳方法
- 适合需要高质量填空题数据的教育AI研究者
我们提出一种名为CQG的生成式方法，从给定文章中构造填空题，重点在于生成多词干扰项。该方法基于语义消歧、文本到文本转换、WordNet的同义词集层级结构与词汇标签，为句子选择答案，并将其分段为若干实例。利用Transformer和同级同义词生成实例级干扰项候选（IDCs），剔除不合适的选项，根据上下文嵌入相似性、同义词集及词汇相关性对剩余候选进行排序。通过组合替换方式生成干扰项，验证其是否为合法短语，最终根据与答案的关键语义相似度选出最优干扰项。实验表明该方法显著优于当前最优水平，人工评估也证实生成干扰项质量优异。
原文摘要 · Abstract (English)
We present a generative method called CQG for constructing cloze questions from a given article using neural networks and WordNet, with an emphasis on generating multigram distractors. Built on sense disambiguation, text-to-text transformation, WordNet's synset taxonomies and lexical labels, CQG selects an answer key for a given sentence, segments it into a sequence of instances, generates instance-level distractor candidates (IDCs) using a transformer and sibling synsets.It then removes inappropriate IDCs, ranks the remaining IDCs based on contextual embedding similarities, as well as synset and lexical relatedness, forms distractor candidates by combinatorially replacing instances with the corresponding top-ranked IDCs, and checks if they are legitimate phrases. Finally, it selects top-ranked distractor candidates based on contextual semantic similarities to the answer key. Experiments show that this method significantly outperforms SOTA results. Human judges also confirm the high qualities of the generated distractors.
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