most citedEvaluating the Effectiveness of Natural Language Inference for Hate Speech Detection in Languages with Limited Labeled Data

1 citations · 1 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CL2024

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

Pia Pachinger, Janis Goldzycher, Anna Maria Planitzer +3

Model interpretability in toxicity detection greatly profits from token-level annotations. However, currently such annotations are only available in English. We introduce a dataset…

econ.GN2024

Censorship in Democracy

Marcel Caesmann, Janis Goldzycher, Matteo Grigoletto +1

The spread of propaganda, misinformation, and biased narratives from autocratic regimes, especially on social media, is a growing concern in many democracies. Can censorship be an…

cs.CL2024

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

Janis Goldzycher, Paul Röttger, Gerold Schneider

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 mode…

cs.CL20231 cited

Evaluating the Effectiveness of Natural Language Inference for Hate Speech Detection in Languages with Limited Labeled Data

Janis Goldzycher, Moritz Preisig, Chantal Amrhein +1

Most research on hate speech detection has focused on English where a sizeable amount of labeled training data is available. However, to expand hate speech detection into more lang…

cs.CL2023

CL-UZH at SemEval-2023 Task 10: Sexism Detection through Incremental Fine-Tuning and Multi-Task Learning with Label Descriptions

Janis Goldzycher

The widespread popularity of social media has led to an increase in hateful, abusive, and sexist language, motivating methods for the automatic detection of such phenomena. The goa…