| --- |
| task_categories: |
| - text-classification |
| language: |
| - de |
| tags: |
| - toxic |
| - offensive |
| - hate |
| - perspectivism |
| - subjective |
| - spans |
| --- |
| |
| # AustroTox: A Dataset for Target-Based Austrian German Offensive Language Detection |
|
|
| This is the data for the paper [AustroTox: A Dataset for Target-Based Austrian German Offensive Language Detection](https://aclanthology.org/2024.findings-acl.713/). |
|
|
|
|
| ## Dataset description |
|
|
| #### Index |
| The index of the comment |
|
|
| #### Article Title |
| The title of the article under which it was posted |
|
|
| #### Comment |
| The comment which was classified |
|
|
| #### Label |
| Denotes whether the aggregated label is Toxic / Offensive (1) or Not toxic / Not offensive (0) |
|
|
| #### Annotators not toxic |
| The annotator ids of the annotators who labelled the text as not toxic / not offensive |
|
|
| #### Annotators toxic |
| The annotator ids of the annotators who labelled the text as toxic / offensive |
|
|
| ### Manually cleaned |
| Indicates whether the annotations were manually cleaned |
|
|
| ### Label fine |
| The fine-grained label of the post |
|
|
| #### Tags |
| The spans and the labels of the spans |
|
|
|
|
|
|
| ## Citation |
|
|
| If you use this dataset, please cite us: |
|
|
| ``` |
| @inproceedings{pachinger-etal-2024-austrotox, |
| title = "{A}ustro{T}ox: A Dataset for Target-Based {A}ustrian {G}erman Offensive Language Detection", |
| author = "Pachinger, Pia and |
| Goldzycher, Janis and |
| Planitzer, Anna and |
| Kusa, Wojciech and |
| Hanbury, Allan and |
| Neidhardt, Julia", |
| editor = "Ku, Lun-Wei and |
| Martins, Andre and |
| Srikumar, Vivek", |
| booktitle = "Findings of the Association for Computational Linguistics: ACL 2024", |
| month = aug, |
| year = "2024", |
| address = "Bangkok, Thailand", |
| publisher = "Association for Computational Linguistics", |
| url = "https://aclanthology.org/2024.findings-acl.713/", |
| doi = "10.18653/v1/2024.findings-acl.713", |
| pages = "11990--12001", |
| 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 Transformer 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." |
| } |
| ``` |
