Multilingual Text Detoxification (TextDetox) 2025
Synopsis
- Task: Given a toxic piece of text, re-write it in a non-toxic way while saving the main content as much as possible.
- Input: tba. [data]
- Output: tba.
- Evaluation: tba.[baselines][leaderboard]
- Submission: tba.
- Registration: tba.
- Contact: [HuggingFace space][google group]
- Paper submission link: tba.
- Paper template: tba.
Task
tba.Methodology
tba.Data
tba.Evaluation
tba.Results
tba.Related Work
- Dementieva D. et al. Methods for Detoxification of Texts for the Russian Language. Multimodal Technologies and Interaction 5, 2021. [pdf]
- Dale D. et. al. Text Detoxification using Large Pre-trained Neural Models. EMNLP, 2021. [pdf]
- Logacheva V. et al. ParaDetox: Detoxification with Parallel Data. ACL, 2022. [pdf]
- Moskovskiy D. et al. Exploring Cross-lingual Text Detoxification with Large Multilingual Language Models. ACL SRW, 2022. [pdf]
- Dementieva D. et al. RUSSE-2022: Findings of the First Russian Detoxification Shared Task Based on Parallel Corpora. Dialogue, 2022. [pdf]
- Logacheva, V. et al. A Study on Manual and Automatic Evaluation for Text Style Transfer: The Case of Detoxification. HumEval, 2022. [pdf]
- Dementieva, D. et al. Exploring Methods for Cross-lingual Text Style Transfer: The Case of Text Detoxification. AACL, 2023. [pdf]
Contributors
The following researchers contributed to the parallel data preparation:- Daryna Dementieva: Ukrainian, English, Russian
- Daniil Moskovskiy: English, Russian
- Florian Schneider: German
- Nikolay Babakov: Ukrainian, Spanish
- Seid Yimam: Amharic
- Abinew Ali Ayele: Amharic
- Ashaf Elnagar: Arabic
- Xinting Wang: Chinese
- Naquee Rizwan: Hindi