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首页> 外文会议>Thirteenth workshop on innovative use of NLP for building educational applications 2018 >Automatic Distractor Suggestion for Multiple-Choice Tests Using Concept Embeddings and Information Retrieval
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Automatic Distractor Suggestion for Multiple-Choice Tests Using Concept Embeddings and Information Retrieval

机译：使用概念嵌入和信息检索进行多项选择测试的自动分心器建议

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摘要

Developing plausible distractors (wrong answer options) when writing multiple-choice questions has been described as one of the most challenging and time-consuming parts of the item-writing process. In this paper we propose a fully automatic method for generating distractor suggestions for multiple-choice questions used in high-stakes medical exams. The system uses a question stem and the correct answer as an input and produces a list of suggested distractors ranked based on their similarity to the stem and the correct answer. To do this we use a novel approach of combining concept embeddings with information retrieval methods. We frame the evaluation as a prediction task where we aim to "predict" the human-produced distractors used in large sets of medical questions, i.e. if a distractor generated by our system is good enough it is likely to feature among the list of distractors produced by the human item-writers. The results reveal that combining concept embeddings with information retrieval approaches significantly improves the generation of plausible distractors and enables us to match around 1 in 5 of the human-produced distractors. The approach proposed in this paper is generalis-able to all scenarios where the distractors refer to concepts.
机译：在编写多项选择题时发展合理的干扰因素（错误的答案选项）被描述为项目编写过程中最具挑战性和最耗时的部分之一。在本文中，我们提出了一种全自动方法，用于针对高风险医学考试中使用的多项选择题生成干扰项建议。系统使用问题词干和正确答案作为输入，并根据其与词干和正确答案的相似性生成建议的干扰因素列表。为此，我们使用了一种将概念嵌入与信息检索方法相结合的新颖方法。我们将评估作为一项预测任务，旨在“预测”在大量医学问题中使用的人工产生的干扰物，即，如果我们系统生成的干扰物足够好，则很可能会出现在产生的干扰物列表中由人类项目作家撰写。结果表明，将概念嵌入与信息检索方法相结合，可显着提高合理的干扰物的生成，并使我们能够匹配五分之一的人工干扰物。本文提出的方法可在所有牵张手涉及概念的情况下通用。

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