Active Learning for Argument Strength Estimation

Nataliia Kees, Michael Fromm, Evgeniy Faerman, Thomas Seidl


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.
Anthology ID:
2021.insights-1.20
Volume:
Proceedings of the Second Workshop on Insights from Negative Results in NLP
Month:
November
Year:
2021
Address:
Online and Punta Cana, Dominican Republic
Venues:
EMNLP | insights
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
144–150
Language:
URL:
https://aclanthology.org/2021.insights-1.20
DOI:
10.18653/v1/2021.insights-1.20
Bibkey:
Cite (ACL):
Nataliia Kees, Michael Fromm, Evgeniy Faerman, and Thomas Seidl. 2021. Active Learning for Argument Strength Estimation. In Proceedings of the Second Workshop on Insights from Negative Results in NLP, pages 144–150, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.
Cite (Informal):
Active Learning for Argument Strength Estimation (Kees et al., insights 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.insights-1.20.pdf
Software:
 2021.insights-1.20.Software.zip
Code
 nkees/active-learning-argument-strength