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:
- 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)
- PDF:
- https://preview.aclanthology.org/corrections-2024-05/2021.germeval-1.14.pdf