Datasets:
metadata
language:
- en
- uk
- ru
- de
- zh
- am
- ar
- hi
- es
license: openrail++
size_categories:
- 1K<n<10K
task_categories:
- text-generation
dataset_info:
features:
- name: toxic_sentence
dtype: string
- name: neutral_sentence
dtype: string
splits:
- name: en
num_bytes: 47435
num_examples: 400
- name: ru
num_bytes: 89453
num_examples: 400
- name: uk
num_bytes: 78106
num_examples: 400
- name: de
num_bytes: 86818
num_examples: 400
- name: es
num_bytes: 56868
num_examples: 400
- name: am
num_bytes: 133489
num_examples: 400
- name: zh
num_bytes: 79089
num_examples: 400
- name: ar
num_bytes: 85237
num_examples: 400
- name: hi
num_bytes: 107518
num_examples: 400
download_size: 489288
dataset_size: 764013
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-*
MultiParaDetox
This is the multilingual parallel dataset for the text detoxification task prepared for CLEF TextDetox 2024 shared task. For each of 9 languages, we collected 1k pairs of toxic<->detoxified instances splitted into two parts: dev (400 pairs) and test (600 pairs).
📰 Updates
[2025] We dived into the explainability of our data in our new COLING paper!
[2024] You can check additional releases for Ukrainian ParaDetox and Spanish ParaDetox from NAACL 2024!
[2024] April, 23rd, update: We are realsing the parallel dev set! The test part for the final phase of the competition is available here!!!
[2022] You can also check previously created training corpora: English ParaDetox from ACL 2022 and Russian ParaDetox.
Toxic Samples Sources
The list of the sources for the original toxic sentences:
- English: Jigsaw, Unitary AI Toxicity Dataset
- Russian: Russian Language Toxic Comments, Toxic Russian Comments
- Ukrainian: Ukrainian Twitter texts
- Spanish: Detecting and Monitoring Hate Speech in Twitter, Detoxis, RoBERTuito: a pre-trained language model for social media text in Spanish
- German: GemEval 2018, 2021
- Amhairc: Amharic Hate Speech
- Arabic: OSACT4
- Hindi: Hostility Detection Dataset in Hindi, Overview of the HASOC track at FIRE 2019: Hate Speech and Offensive Content Identification in Indo-European Languages
Citation
If you would like to acknowledge our work, please, cite the following manuscripts:
@inproceedings{dementieva-etal-2025-multilingual,
title = "Multilingual and Explainable Text Detoxification with Parallel Corpora",
author = "Dementieva, Daryna and
Babakov, Nikolay and
Ronen, Amit and
Ayele, Abinew Ali and
Rizwan, Naquee and
Schneider, Florian and
Wang, Xintong and
Yimam, Seid Muhie and
Moskovskiy, Daniil Alekhseevich and
Stakovskii, Elisei and
Kaufman, Eran and
Elnagar, Ashraf and
Mukherjee, Animesh and
Panchenko, Alexander",
editor = "Rambow, Owen and
Wanner, Leo and
Apidianaki, Marianna and
Al-Khalifa, Hend and
Eugenio, Barbara Di and
Schockaert, Steven",
booktitle = "Proceedings of the 31st International Conference on Computational Linguistics",
month = jan,
year = "2025",
address = "Abu Dhabi, UAE",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.coling-main.535/",
pages = "7998--8025",
abstract = "Even with various regulations in place across countries and social media platforms (Government of India, 2021; European Parliament and Council of the European Union, 2022), digital abusive speech remains a significant issue. One potential approach to address this challenge is automatic text detoxification, a text style transfer (TST) approach that transforms toxic language into a more neutral or non-toxic form. To date, the availability of parallel corpora for the text detoxification task (Logacheva et al., 2022; Atwell et al., 2022; Dementieva et al., 2024a) has proven to be crucial for state-of-the-art approaches. With this work, we extend parallel text detoxification corpus to new languages{---}German, Chinese, Arabic, Hindi, and Amharic{---}testing in the extensive multilingual setup TST baselines. Next, we conduct the first of its kind an automated, explainable analysis of the descriptive features of both toxic and non-toxic sentences, diving deeply into the nuances, similarities, and differences of toxicity and detoxification across 9 languages. Finally, based on the obtained insights, we experiment with a novel text detoxification method inspired by the Chain-of-Thoughts reasoning approach, enhancing the prompting process through clustering on relevant descriptive attributes."
}
@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{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}
}