Towards Low-Budget Real-Time Active Learning for Text Classification via Proxy-Based Data Selection

Jakob Andersen, Olaf Zukunft

2023

Abstract

Training data is typically the bottleneck of supervised machine learning applications, heavily relying on cost-intensive human annotations. Active Learning proposes an interactive framework to efficiently spend human efforts in the training data generation process. However, re-training state-of-the-art text classifiers is highly computationally intensive, leading to long training cycles that cause annoying interruptions to humans in the loop. To enhance the applicability of Active Learning, we investigate low-budget real-time Active Learning via Proxy-based data selection in the domain of text classification. We aim to enable fast interactive cycles within a minimal labelling effort while exploiting the performance of state-of-the-art text classifiers. Our results show that Proxy-based Active Learning can increase the F1-score of a lightweight classifier compared to a traditional budget Active Learning approach up to ~19%. Our novel Proxy-based Active Learning approach can be carried out time-efficiently, requiring less than 1 second for each learning iteration.

Paper Citation

in Harvard Style

Andersen J. and Zukunft O. (2023). Towards Low-Budget Real-Time Active Learning for Text Classification via Proxy-Based Data Selection. In Proceedings of the 15th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART, ISBN 978-989-758-623-1, pages 25-33. DOI: 10.5220/0011606000003393

in Bibtex Style

@conference{icaart23,
author={Jakob Andersen and Olaf Zukunft},
title={Towards Low-Budget Real-Time Active Learning for Text Classification via Proxy-Based Data Selection},
booktitle={Proceedings of the 15th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART,},
year={2023},
pages={25-33},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011606000003393},
isbn={978-989-758-623-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 15th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART,
TI - Towards Low-Budget Real-Time Active Learning for Text Classification via Proxy-Based Data Selection
SN - 978-989-758-623-1
AU - Andersen J.
AU - Zukunft O.
PY - 2023
SP - 25
EP - 33
DO - 10.5220/0011606000003393