AustroTox: A Dataset for Target-Based Austrian German Offensive Language Detection
arxiv(2024)
Abstract
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 German dialect,
comprising 4,562 user comments. In addition to binary offensiveness
classification, we identify spans within each comment constituting vulgar
language or representing targets of offensive statements. We evaluate
fine-tuned language models as well as large language models in a zero- and
few-shot fashion. The results indicate that while fine-tuned models excel in
detecting linguistic peculiarities such as vulgar dialect, large language
models demonstrate superior performance in detecting offensiveness in
AustroTox. We publish the data and code.
MoreTranslated text
AI Read Science
Must-Reading Tree
Example
![](https://originalfileserver.aminer.cn/sys/aminer/pubs/mrt_preview.jpeg)
Generate MRT to find the research sequence of this paper
Chat Paper
Summary is being generated by the instructions you defined