  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.
