Active Learning for Argument Strength Estimation
Nataliia Kees, Michael Fromm, Evgeniy Faerman, Thomas Seidl · 2021
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.