--- language: - en - uk - ru - de - zh - am - ar - hi - es license: openrail++ size_categories: - 1Kdetoxified 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](https://huggingface.co./papers/2412.11691)! **[2024]** You can check additional releases for [Ukrainian ParaDetox](https://huggingface.co./datasets/textdetox/uk_paradetox) and [Spanish ParaDetox](https://huggingface.co./datasets/textdetox/es_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](https://huggingface.co./datasets/textdetox/multilingual_paradetox_test)!!!** **[2022]** You can also check previously created training corpora: [English ParaDetox](https://huggingface.co./datasets/s-nlp/paradetox) from ACL 2022 and [Russian ParaDetox](https://huggingface.co./datasets/s-nlp/ru_paradetox). ## Toxic Samples Sources The list of the sources for the original toxic sentences: * 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: [Ukrainian Twitter texts](https://github.com/saganoren/ukr-twi-corpus) * Spanish: [Detecting and Monitoring Hate Speech in Twitter](https://www.mdpi.com/1424-8220/19/21/4654), [Detoxis](https://rdcu.be/dwhxH), [RoBERTuito: a pre-trained language model for social media text in Spanish](https://aclanthology.org/2022.lrec-1.785/) * German: [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) ## 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} } ```