no code implementations • GermEval 2021 • Mina Schütz, Christoph Demus, Jonas Pitz, Nadine Probol, Melanie Siegel, Dirk Labudde
For this binary task, we propose three models: a German BERT transformer model; a multilayer perceptron, which was first trained in parallel on textual input and 14 additional linguistic features and then concatenated in an additional layer; and a multilayer perceptron with both feature types as input.
no code implementations • GermEval 2021 • Jaqueline Böck, Daria Liakhovets, Mina Schütz, Armin Kirchknopf, Djordje Slijepčević, Matthias Zeppelzauer, Alexander Schindler
Our best model is GottBERT (i. e., a BERT transformer pre-trained on German texts) fine-tuned on the GermEval 2021 data.
1 code implementation • NAACL (WOAH) 2022 • Christoph Demus, Jonas Pitz, Mina Schütz, Nadine Probol, Melanie Siegel, Dirk Labudde
In this work, we present a new publicly available offensive language dataset of 10. 278 German social media comments collected in the first half of 2021 that were annotated by in total six annotators.
no code implementations • 9 Jun 2021 • Mina Schütz, Jaqueline Boeck, Daria Liakhovets, Djordje Slijepčević, Armin Kirchknopf, Manuel Hecht, Johannes Bogensperger, Sven Schlarb, Alexander Schindler, Matthias Zeppelzauer
For both tasks our best model is XLM-R with unsupervised pre-training on the EXIST data and additional datasets and fine-tuning on the provided dataset.