Datasets:
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---
language:
- en
- ru
- uk
- de
- es
- am
- zh
- ar
- hi
license: openrail++
size_categories:
- 10K<n<100K
task_categories:
- text-classification
dataset_info:
features:
- name: text
dtype: string
- name: toxic
dtype: int64
splits:
- name: en
num_bytes: 411178
num_examples: 5000
- name: ru
num_bytes: 710001
num_examples: 5000
- name: uk
num_bytes: 630595
num_examples: 5000
- name: de
num_bytes: 941017
num_examples: 5000
- name: es
num_bytes: 978750
num_examples: 5000
- name: am
num_bytes: 1102628
num_examples: 5000
- name: zh
num_bytes: 359235
num_examples: 5000
- name: ar
num_bytes: 889661
num_examples: 5000
- name: hi
num_bytes: 1842662
num_examples: 5000
download_size: 4470012
dataset_size: 7865727
configs:
- config_name: default
data_files:
- split: en
path: data/en-*
- split: ru
path: data/ru-*
- split: uk
path: data/uk-*
- split: de
path: data/de-*
- split: es
path: data/es-*
- split: am
path: data/am-*
- split: zh
path: data/zh-*
- split: ar
path: data/ar-*
- split: hi
path: data/hi-*
---
For the shared task [CLEF TextDetox 2024](https://pan.webis.de/clef24/pan24-web/text-detoxification.html), we provide a compilation of binary toxicity classification datasets for each language.
Namely, for each language, we provide 5k subparts of the datasets -- 2.5k toxic and 2.5k non-toxic samples.
The list of original sources:
* English: [Jigsaw](https://www.kaggle.com/c/jigsaw-toxic-comment-classification-challenge), [Unitary AI Toxicity Dataset](https://github.com/unitaryai/detoxify)
* Russian: [Russian Language Toxic Comments](https://www.kaggle.com/datasets/blackmoon/russian-language-toxic-comments), [Toxic Russian Comments](https://www.kaggle.com/datasets/alexandersemiletov/toxic-russian-comments)
* Ukrainian: our labeling based on [Ukrainian Twitter texts](https://github.com/saganoren/ukr-twi-corpus)
* Spanish: [CLANDESTINO, the Spanish toxic language dataset](https://github.com/microsoft/Clandestino/tree/main)
* German: [DeTox-Dataset](https://github.com/hdaSprachtechnologie/detox), [GemEval 2018, 2021](https://aclanthology.org/2021.germeval-1.1/)
* Amhairc: [Amharic Hate Speech](https://github.com/uhh-lt/AmharicHateSpeech)
* Arabic: [OSACT4](https://edinburghnlp.inf.ed.ac.uk/workshops/OSACT4/)
* Hindi: [Hostility Detection Dataset in Hindi](https://competitions.codalab.org/competitions/26654#learn_the_details-dataset), [Overview of the HASOC track at FIRE 2019: Hate Speech and Offensive Content Identification in Indo-European Languages](https://dl.acm.org/doi/pdf/10.1145/3368567.3368584?download=true)
All credits go to the authors of the original toxic words lists.
## Citation
If you would like to acknowledge our work, please, cite the following manuscripts:
```
@inproceedings{dementieva2024overview,
title={Overview of the Multilingual Text Detoxification Task at PAN 2024},
author={Dementieva, Daryna and Moskovskiy, Daniil and Babakov, Nikolay and Ayele, Abinew Ali and Rizwan, Naquee and Schneider, Frolian and Wang, Xintog and Yimam, Seid Muhie and Ustalov, Dmitry and Stakovskii, Elisei and Smirnova, Alisa and Elnagar, Ashraf and Mukherjee, Animesh and Panchenko, Alexander},
booktitle={Working Notes of CLEF 2024 - Conference and Labs of the Evaluation Forum},
editor={Guglielmo Faggioli and Nicola Ferro and Petra Galu{\v{s}}{\v{c}}{\'a}kov{\'a} and Alba Garc{\'i}a Seco de Herrera},
year={2024},
organization={CEUR-WS.org}
}
```
```
@inproceedings{dementieva-etal-2024-toxicity,
title = "Toxicity Classification in {U}krainian",
author = "Dementieva, Daryna and
Khylenko, Valeriia and
Babakov, Nikolay and
Groh, Georg",
editor = {Chung, Yi-Ling and
Talat, Zeerak and
Nozza, Debora and
Plaza-del-Arco, Flor Miriam and
R{\"o}ttger, Paul and
Mostafazadeh Davani, Aida and
Calabrese, Agostina},
booktitle = "Proceedings of the 8th Workshop on Online Abuse and Harms (WOAH 2024)",
month = jun,
year = "2024",
address = "Mexico City, Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.woah-1.19/",
doi = "10.18653/v1/2024.woah-1.19",
pages = "244--255",
abstract = "The task of toxicity detection is still a relevant task, especially in the context of safe and fair LMs development. Nevertheless, labeled binary toxicity classification corpora are not available for all languages, which is understandable given the resource-intensive nature of the annotation process. Ukrainian, in particular, is among the languages lacking such resources. To our knowledge, there has been no existing toxicity classification corpus in Ukrainian. In this study, we aim to fill this gap by investigating cross-lingual knowledge transfer techniques and creating labeled corpora by: (i){\textasciitilde}translating from an English corpus, (ii){\textasciitilde}filtering toxic samples using keywords, and (iii){\textasciitilde}annotating with crowdsourcing. We compare LLMs prompting and other cross-lingual transfer approaches with and without fine-tuning offering insights into the most robust and efficient baselines."
}
```
```
@inproceedings{DBLP:conf/ecir/BevendorffCCDEFFKMMPPRRSSSTUWZ24,
author = {Janek Bevendorff and
Xavier Bonet Casals and
Berta Chulvi and
Daryna Dementieva and
Ashaf Elnagar and
Dayne Freitag and
Maik Fr{\"{o}}be and
Damir Korencic and
Maximilian Mayerl and
Animesh Mukherjee and
Alexander Panchenko and
Martin Potthast and
Francisco Rangel and
Paolo Rosso and
Alisa Smirnova and
Efstathios Stamatatos and
Benno Stein and
Mariona Taul{\'{e}} and
Dmitry Ustalov and
Matti Wiegmann and
Eva Zangerle},
editor = {Nazli Goharian and
Nicola Tonellotto and
Yulan He and
Aldo Lipani and
Graham McDonald and
Craig Macdonald and
Iadh Ounis},
title = {Overview of {PAN} 2024: Multi-author Writing Style Analysis, Multilingual
Text Detoxification, Oppositional Thinking Analysis, and Generative
{AI} Authorship Verification - Extended Abstract},
booktitle = {Advances in Information Retrieval - 46th European Conference on Information
Retrieval, {ECIR} 2024, Glasgow, UK, March 24-28, 2024, Proceedings,
Part {VI}},
series = {Lecture Notes in Computer Science},
volume = {14613},
pages = {3--10},
publisher = {Springer},
year = {2024},
url = {https://doi.org/10.1007/978-3-031-56072-9\_1},
doi = {10.1007/978-3-031-56072-9\_1},
timestamp = {Fri, 29 Mar 2024 23:01:36 +0100},
biburl = {https://dblp.org/rec/conf/ecir/BevendorffCCDEFFKMMPPRRSSSTUWZ24.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
``` |