Active Learning for Argument Strength Estimation
This addresses the high annotation cost in argument mining, but the results are incremental as they show no improvement over baseline methods.
The study tested uncertainty-based active learning methods on two argument-strength datasets to reduce annotation effort, but found they did not surpass random acquisition in accuracy.
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.