Active Learning for Argument Mining: A Practical Approach

by   Nikolai Solmsdorf, et al.

Despite considerable recent progress, the creation of well-balanced and diverse resources remains a time-consuming and costly challenge in Argument Mining. Active Learning reduces the amount of data necessary for the training of machine learning models by querying the most informative samples for annotation and therefore is a promising method for resource creation. In a large scale comparison of several Active Learning methods, we show that Active Learning considerably decreases the effort necessary to get good deep learning performance on the task of Argument Unit Recognition and Classification (AURC).


page 20

page 21

page 22

page 24

page 25

page 26


Active Learning for Argument Strength Estimation

High-quality arguments are an essential part of decision-making. Automat...

How useful is Active Learning for Image-based Plant Phenotyping?

Deep learning models have been successfully deployed for a diverse array...

Efficient Argument Structure Extraction with Transfer Learning and Active Learning

The automation of extracting argument structures faces a pair of challen...

On the Evaluation Criterions for the Active Learning Processes

In many data mining applications collection of sufficiently large datase...

On the Effect of Sample and Topic Sizes for Argument Mining Datasets

The task of Argument Mining, that is extracting argumentative sentences ...

Nuclear Discrepancy for Active Learning

Active learning algorithms propose which unlabeled objects should be que...

Responsible Active Learning via Human-in-the-loop Peer Study

Active learning has been proposed to reduce data annotation efforts by o...

Please sign up or login with your details

Forgot password? Click here to reset