论文标题
在标准测试中为多项选择问题的自动分散术者生成
Automatic Distractor Generation for Multiple Choice Questions in Standard Tests
论文作者
论文摘要
为了评估学习者的知识水平，多项选择问题是标准测试中有效而普遍的形式。但是，多项选择问题的组成，尤其是构建干扰物是非常具有挑战性的。干扰因素必须使不正确且合理地使他们混淆没有掌握知识的学习者。目前，干扰素是由既昂贵又耗时的领域专家产生的。这敦促出现自动分散分心器的出现，这可以使各种域中的各种标准测试受益。在本文中，我们提出了一个问答，并回答引导的干扰物产生（EDGE）框架，以使分散器的产生自动化。边缘由三个主要模块组成：（1）改革问题模块和改革通道模块应用门层，以确保产生的干扰物的固有错误； （2）干扰物发生器模块应用了注意机制来控制合理性水平。大规模公共数据集的实验结果表明，我们的模型大大优于现有模型，并实现了新的最新模型。
To assess the knowledge proficiency of a learner, multiple choice question is an efficient and widespread form in standard tests. However, the composition of the multiple choice question, especially the construction of distractors is quite challenging. The distractors are required to both incorrect and plausible enough to confuse the learners who did not master the knowledge. Currently, the distractors are generated by domain experts which are both expensive and time-consuming. This urges the emergence of automatic distractor generation, which can benefit various standard tests in a wide range of domains. In this paper, we propose a question and answer guided distractor generation (EDGE) framework to automate distractor generation. EDGE consists of three major modules: (1) the Reforming Question Module and the Reforming Passage Module apply gate layers to guarantee the inherent incorrectness of the generated distractors; (2) the Distractor Generator Module applies attention mechanism to control the level of plausibility. Experimental results on a large-scale public dataset demonstrate that our model significantly outperforms existing models and achieves a new state-of-the-art.
