lschoen/germeval21_detox

Tasks

• text-classification

Citation text from the card

Based on the work by Schütz et al.

```biblatex
{schutz-etal-2021-detox,
  title = {{{DeTox}} at {{GermEval}} 2021: {{Toxic}} Comment Classification},
  booktitle = {Proceedings of the {{GermEval}} 2021 Shared Task on the Identification of Toxic, Engaging, and Fact-Claiming Comments},
  author = {Schütz, Mina and Demus, Christoph and Pitz, Jonas and Probol, Nadine and Siegel, Melanie and Labudde, Dirk},
  editor = {Risch, Julian and Stoll, Anke and Wilms, Lena and Wiegand, Michael},
  date = {2021-09},
  pages = {54--61},
  publisher = {Association for Computational Linguistics},
  location = {Duesseldorf, Germany},
  url = {https://aclanthology.org/2021.germeval-1.8},
  abstract = {In this work, we present our approaches on the toxic comment classification task (subtask 1) of the GermEval 2021 Shared Task. 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. We enhanced our pre-trained transformer model by re-training it with over 1 million tweets and fine-tuned it on two additional German datasets of similar tasks. The embeddings of the final fine-tuned German BERT were taken as the textual input features for our neural networks. Our best models on the validation data were both neural networks, however our enhanced German BERT 

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