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README.md
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license: cc-by-4.0
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---
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---
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license: cc-by-4.0
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task_categories:
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- text-classification
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language:
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- de
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tags:
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- hate-speech-detection
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- hate-speech
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pretty_name: GAHD
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size_categories:
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- 10K<n<100K
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---
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# Dataset Card for GAHD
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## Dataset Description
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GAHD is a **G**erman **A**dversarial **H**ate speech **D**ataset containing 10,996 examples. We collected the dataset via four rounds of Dynamic Adversarial Data Collection and explored various methods of supporting annotators in finding adversarial examples.
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- **Paper:** https://arxiv.org/abs/2403.19559
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- **Repository:** https://github.com/jagol/gahd
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## Dataset Structure
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`gahd.csv` contains the following columns:
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- `gahd_id`: unique identifier of the entry
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- `text`: text of the entry
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- `label`: `0` = "not-hate speech", `1` = "hate speech"
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- `round`: round in which the entry was created
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- `split`: "train", "dev", or "test"
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- `contrastive_gahd_id`: `gahd_id` of its contrastive example
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`gahd_disaggregated.csv` contains the following additional columns:
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- `source`:
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- if annotators entered the entry via the Dynabench interface: `dynabench`
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- if the entry was translated from the Vidgen et al. 2021 dataset: `translation`
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- if the entry stems from the Leipzit news corpus: `news`
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- `model_prediction`: label predicted by the target model, `0` or `1`
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- `annotator_id`: unique identifier of the annotator that created the entry
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- `annotator_labels`: a string containing a forward slash-separated list of all labels by annotators
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- `expert_labels`: `0` or `1` if an expert annotator annotated the entry, otherwise empty
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## Citation
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When using GAHD, please cite our preprint on Arxiv:
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```
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@misc{goldzycher2024improving,
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title={Improving Adversarial Data Collection by Supporting Annotators: Lessons from GAHD, a German Hate Speech Dataset},
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author={Janis Goldzycher and Paul Röttger and Gerold Schneider},
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year={2024},
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eprint={2403.19559},
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archivePrefix={arXiv},
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primaryClass={cs.CL}
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}
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```
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