Accepted paper at EMNLP 2021 Workshop
Active Learning for Argument Strength Estimation
27.09.2021
Authors
Nataliia Kees, Michael Fromm, Evgeniy Faerman, Thomas Seidl
The 2nd Workshop on Insights from Negative Results
colocated with the 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP 2021),
07–11 November 2021, Punta Cana, Dominican Republic
Abstract
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. [arXiv]