基于主动学习的论元强度估计
Active Learning for Argument Strength Estimation
摘要：
高质量的论证是决策的重要组成部分。自动预测论点的质量是一个复杂的任务，最近得到了很多关注的论点挖掘。然而，这个任务的注释工作量非常高。因此，我们测试基于不确定性的主动学习（AL）方法在两个流行的参数强度数据集，以估计是否可以启用样本有效的学习。我们广泛的经验评估表明，基于不确定性的采集功能不能超过这些数据集上的随机采集所达到的精度。
英文摘要：
High-quality arguments are an essential part of decision-making. Automatically predicting the quality of an argument is a complex task that recently got much attention in argument mining. However, the annotation effort for this task is exceptionally high. Therefore, we test uncertainty-based active learning (AL) methods on two popular argument-strength data sets to estimate whether sample-efficient learning can be enabled. Our extensive empirical evaluation shows that uncertainty-based acquisition functions can not surpass the accuracy reached with the random acquisition on these data sets.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
