Multilingual Text Detoxification (TextDetox) 2025

Stop the war!

Sponsored by
Toloka

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.
  1. Dementieva D. et al. Methods for Detoxification of Texts for the Russian Language. Multimodal Technologies and Interaction 5, 2021. [pdf]
  2. Dale D. et. al. Text Detoxification using Large Pre-trained Neural Models. EMNLP, 2021. [pdf]
  3. Logacheva V. et al. ParaDetox: Detoxification with Parallel Data. ACL, 2022. [pdf]
  4. Moskovskiy D. et al. Exploring Cross-lingual Text Detoxification with Large Multilingual Language Models. ACL SRW, 2022. [pdf]
  5. Dementieva D. et al. RUSSE-2022: Findings of the First Russian Detoxification Shared Task Based on Parallel Corpora. Dialogue, 2022. [pdf]
  6. Logacheva, V. et al. A Study on Manual and Automatic Evaluation for Text Style Transfer: The Case of Detoxification. HumEval, 2022. [pdf]
  7. 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

Task Committee