UR@NLP_A_Team @ GermEval 2021: Ensemble-based Classification of Toxic, Engaging and Fact-Claiming Comments

Kwabena Odame Akomeah, Udo Kruschwitz, Bernd Ludwig

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Abstract
In this paper, we report on our approach to addressing the GermEval 2021 Shared Task on the Identification of Toxic, Engaging, and Fact-Claiming Comments for the German language. We submitted three runs for each subtask based on ensembles of three models each using contextual embeddings from pre-trained language models using SVM and neural-network-based classifiers. We include language-specific as well as language-agnostic language models – both with and without fine-tuning. We observe that for the runs we submitted that the SVM models overfitted the training data and this affected the aggregation method (simple majority voting) of the ensembles. The model records a lower performance on the test set than on the training set. Exploring the issue of overfitting we uncovered that due to a bug in the pipeline the runs we submitted had not been trained on the full set but only on a small training set. Therefore in this paper we also include the results we get when trained on the full training set which demonstrate the power of ensembles.
Anthology ID:
2021.germeval-1.14
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:
95–99
Language:
URL:
https://aclanthology.org/2021.germeval-1.14
DOI:
Bibkey:
Cite (ACL):
Kwabena Odame Akomeah, Udo Kruschwitz, and Bernd Ludwig. 2021. UR@NLP_A_Team @ GermEval 2021: Ensemble-based Classification of Toxic, Engaging and Fact-Claiming Comments. In Proceedings of the GermEval 2021 Shared Task on the Identification of Toxic, Engaging, and Fact-Claiming Comments, pages 95–99, Duesseldorf, Germany. Association for Computational Linguistics.
Cite (Informal):
UR@NLP_A_Team @ GermEval 2021: Ensemble-based Classification of Toxic, Engaging and Fact-Claiming Comments (Akomeah et al., GermEval 2021)
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PDF:
https://preview.aclanthology.org/teach-a-man-to-fish/2021.germeval-1.14.pdf
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