Revisiting Uncertainty-based Query Strategies for Active Learning with Transformers

Christopher Schröder, Andreas Niekler, Martin Potthast


Abstract
Active learning is the iterative construction of a classification model through targeted labeling, enabling significant labeling cost savings. As most research on active learning has been carried out before transformer-based language models (“transformers”) became popular, despite its practical importance, comparably few papers have investigated how transformers can be combined with active learning to date. This can be attributed to the fact that using state-of-the-art query strategies for transformers induces a prohibitive runtime overhead, which effectively nullifies, or even outweighs the desired cost savings. For this reason, we revisit uncertainty-based query strategies, which had been largely outperformed before, but are particularly suited in the context of fine-tuning transformers. In an extensive evaluation, we connect transformers to experiments from previous research, assessing their performance on five widely used text classification benchmarks. For active learning with transformers, several other uncertainty-based approaches outperform the well-known prediction entropy query strategy, thereby challenging its status as most popular uncertainty baseline in active learning for text classification.
Anthology ID:
2022.findings-acl.172
Volume:
Findings of the Association for Computational Linguistics: ACL 2022
Month:
May
Year:
2022
Address:
Dublin, Ireland
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2194–2203
Language:
URL:
https://aclanthology.org/2022.findings-acl.172
DOI:
10.18653/v1/2022.findings-acl.172
Bibkey:
Cite (ACL):
Christopher Schröder, Andreas Niekler, and Martin Potthast. 2022. Revisiting Uncertainty-based Query Strategies for Active Learning with Transformers. In Findings of the Association for Computational Linguistics: ACL 2022, pages 2194–2203, Dublin, Ireland. Association for Computational Linguistics.
Cite (Informal):
Revisiting Uncertainty-based Query Strategies for Active Learning with Transformers (Schröder et al., Findings 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.findings-acl.172.pdf
Video:
 https://aclanthology.org/2022.findings-acl.172.mp4
Code
 webis-de/acl-22
Data
AG NewsMRSUBJTREC-10