Abstract
This paper describes our approach (ur-iw-hnt) for the Shared Task of GermEval2021 to identify toxic, engaging, and fact-claiming comments. We submitted three runs using an ensembling strategy by majority (hard) voting with multiple different BERT models of three different types: German-based, Twitter-based, and multilingual models. All ensemble models outperform single models, while BERTweet is the winner of all individual models in every subtask. Twitter-based models perform better than GermanBERT models, and multilingual models perform worse but by a small margin.- Anthology ID:
- 2021.germeval-1.12
- 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:
- 83–87
- Language:
- URL:
- https://aclanthology.org/2021.germeval-1.12
- DOI:
- Cite (ACL):
- Hoai Nam Tran and Udo Kruschwitz. 2021. ur-iw-hnt at GermEval 2021: An Ensembling Strategy with Multiple BERT Models. In Proceedings of the GermEval 2021 Shared Task on the Identification of Toxic, Engaging, and Fact-Claiming Comments, pages 83–87, Duesseldorf, Germany. Association for Computational Linguistics.
- Cite (Informal):
- ur-iw-hnt at GermEval 2021: An Ensembling Strategy with Multiple BERT Models (Tran & Kruschwitz, GermEval 2021)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2021.germeval-1.12.pdf