Janis Goldzycher — accepted AI-conference papers

2024

AustroTox: A Dataset for Target-Based Austrian German Offensive Language Detection

Pia Pachinger, Janis Goldzycher, Anna Planitzer, Wojciech Kusa, Allan Hanbury, Julia Neidhardt

ACL 2024findings

Model interpretability in toxicity detection greatly profits from token-level annotations. However, currently, such annotations are only available in English. We introduce a dataset annotated for offensive language detection sourced from a news forum, notable for its incorporation of the Austrian Ge…

Cited by 3 Source ↗PDF ↗code ↗Scholar ↗

2024

Improving Adversarial Data Collection by Supporting Annotators: Lessons from GAHD, a German Hate Speech Dataset

Janis Goldzycher, Paul Röttger, Gerold Schneider

NAACL 2024long

Hate speech detection models are only as good as the data they are trained on. Datasets sourced from social media suffer from systematic gaps and biases, leading to unreliable models with simplistic decision boundaries. Adversarial datasets, collected by exploiting model weaknesses, promise to fix t…

Cited by 9 Source ↗PDF ↗code ↗Scholar ↗