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Active Learning for Argument Strength Estimation

MCML Authors

Link to Profile Thomas Seidl

Thomas Seidl

Prof. Dr.

Director

Database Systems, Data Mining and AI

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.

inproceedings KFF+21

Insights @EMNLP 2021

2nd Workshop on Insights from Negative Results at the Conference on Empirical Methods in Natural Language Processing. Punta Cana, Dominican Republic, Nov 07-11, 2021.

Authors

N. Kees • M. Fromm • E. Faerman • T. Seidl

Links

DOI

Research Area

 A3 | Computational Models

BibTeXKey: KFF+21

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