@inproceedings{kees-etal-2021-active,
title = "Active Learning for Argument Strength Estimation",
author = "Kees, Nataliia and
Fromm, Michael and
Faerman, Evgeniy and
Seidl, Thomas",
editor = "Sedoc, Jo{\~a}o and
Rogers, Anna and
Rumshisky, Anna and
Tafreshi, Shabnam",
booktitle = "Proceedings of the Second Workshop on Insights from Negative Results in NLP",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.insights-1.20",
doi = "10.18653/v1/2021.insights-1.20",
pages = "144--150",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Active Learning for Argument Strength Estimation
%A Kees, Nataliia
%A Fromm, Michael
%A Faerman, Evgeniy
%A Seidl, Thomas
%Y Sedoc, João
%Y Rogers, Anna
%Y Rumshisky, Anna
%Y Tafreshi, Shabnam
%S Proceedings of the Second Workshop on Insights from Negative Results in NLP
%D 2021
%8 November
%I Association for Computational Linguistics
%C Online and Punta Cana, Dominican Republic
%F kees-etal-2021-active
%X 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.
%R 10.18653/v1/2021.insights-1.20
%U https://aclanthology.org/2021.insights-1.20
%U https://doi.org/10.18653/v1/2021.insights-1.20
%P 144-150
Markdown (Informal)
[Active Learning for Argument Strength Estimation](https://aclanthology.org/2021.insights-1.20) (Kees et al., insights 2021)
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.