论文标题
没有教师的迭代机器教学
Iterative Machine Teaching without Teachers
论文作者
论文摘要
迭代机学教学是一种选择最佳教学示例的方法，该示例使学生能够在每次迭代中有效地学习目标概念。现有关于迭代机械教学的研究基于监督的机器学习，并假设有些教师知道所有教学示例的真实答案。在这项研究中，我们考虑了一个不存在这样的老师的无监督案例。也就是说，我们无法访问任何教学示例的真实答案。在每次迭代中为学生提供一个教学示例，但是不能保证相应的标签是否正确。关于众包的最新研究开发了估计众包回答的真实答案的方法。在这项研究中，我们将这些应用于迭代机械教学，以估算教学示例的真实标签以及用于教学的学生模型。我们的方法支持没有老师的学生的协作学习。实验结果表明，我们方法的教学表现尤其对低级学生特别有效。
Iterative machine teaching is a method for selecting an optimal teaching example that enables a student to efficiently learn a target concept at each iteration. Existing studies on iterative machine teaching are based on supervised machine learning and assume that there are teachers who know the true answers of all teaching examples. In this study, we consider an unsupervised case where such teachers do not exist; that is, we cannot access the true answer of any teaching example. Students are given a teaching example at each iteration, but there is no guarantee if the corresponding label is correct. Recent studies on crowdsourcing have developed methods for estimating the true answers from crowdsourcing responses. In this study, we apply these to iterative machine teaching for estimating the true labels of teaching examples along with student models that are used for teaching. Our method supports the collaborative learning of students without teachers. The experimental results show that the teaching performance of our method is particularly effective for low-level students in particular.
