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.
@article{arxiv.2109.11319,
title = {Active Learning for Argument Strength Estimation},
author = {Nataliia Kees and Michael Fromm and Evgeniy Faerman and Thomas Seidl},
journal= {arXiv preprint arXiv:2109.11319},
year = {2021}
}