FHAC at GermEval 2021: Identifying German toxic, engaging, and fact-claiming comments with ensemble learning

Tobias Bornheim, Niklas Grieger, Stephan Bialonski


Abstract
The availability of language representations learned by large pretrained neural network models (such as BERT and ELECTRA) has led to improvements in many downstream Natural Language Processing tasks in recent years. Pretrained models usually differ in pretraining objectives, architectures, and datasets they are trained on which can affect downstream performance. In this contribution, we fine-tuned German BERT and German ELECTRA models to identify toxic (subtask 1), engaging (subtask 2), and fact-claiming comments (subtask 3) in Facebook data provided by the GermEval 2021 competition. We created ensembles of these models and investigated whether and how classification performance depends on the number of ensemble members and their composition. On out-of-sample data, our best ensemble achieved a macro-F1 score of 0.73 (for all subtasks), and F1 scores of 0.72, 0.70, and 0.76 for subtasks 1, 2, and 3, respectively.
Anthology ID:
2021.germeval-1.16
Volume:
Proceedings of the GermEval 2021 Shared Task on the Identification of Toxic, Engaging, and Fact-Claiming Comments
Month:
September
Year:
2021
Address:
Duesseldorf, Germany
Editors:
Julian Risch, Anke Stoll, Lena Wilms, Michael Wiegand
Venue:
GermEval
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
105–111
Language:
URL:
https://aclanthology.org/2021.germeval-1.16
DOI:
Bibkey:
Cite (ACL):
Tobias Bornheim, Niklas Grieger, and Stephan Bialonski. 2021. FHAC at GermEval 2021: Identifying German toxic, engaging, and fact-claiming comments with ensemble learning. In Proceedings of the GermEval 2021 Shared Task on the Identification of Toxic, Engaging, and Fact-Claiming Comments, pages 105–111, Duesseldorf, Germany. Association for Computational Linguistics.
Cite (Informal):
FHAC at GermEval 2021: Identifying German toxic, engaging, and fact-claiming comments with ensemble learning (Bornheim et al., GermEval 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.germeval-1.16.pdf
Code
 dslaborg/germeval2021
Data
GermEval