论文标题
OBJ2SUB：无监督的目标转换为主观问题
Obj2Sub: Unsupervised Conversion of Objective to Subjective Questions
论文作者
论文摘要
进行考试是为了测试学习者对学科的理解。为了防止学习者猜测或交换解决方案，管理的测试方式必须具有足够的主观问题，可以通过要求详细的答案来衡量学习者是否已经理解了这一概念。因此，在本文中，我们提出了一种新型混合无监督的方法，利用基于规则的方法和预训练的密集检索器，以自动将客观问题转换为主观问题的新任务。我们观察到，我们的方法的表现优于现有数据驱动的方法，该方法通过召回@k和precision@k衡量。
Exams are conducted to test the learner's understanding of the subject. To prevent the learners from guessing or exchanging solutions, the mode of tests administered must have sufficient subjective questions that can gauge whether the learner has understood the concept by mandating a detailed answer. Hence, in this paper, we propose a novel hybrid unsupervised approach leveraging rule-based methods and pre-trained dense retrievers for the novel task of automatically converting the objective questions to subjective questions. We observe that our approach outperforms the existing data-driven approaches by 36.45% as measured by Recall@k and Precision@k.
