task_categories:text-classification language:en license:openrail++ region:us
---
license: openrail++
task_categories:
- text-classification
language:
- en
---

# ParaDetox: Detoxification with Parallel Data (English). Paraphrase Task Negative Results

This repository contains information about **Paraphrase Task** markup from [English Paradetox dataset](https://huggingface.co/datasets/s-nlp/paradetox) collection pipeline.
In this dataset, the samples that were marked as *"cannot rewrite"* are present.
The original paper ["ParaDetox: Detoxification with Parallel Data"](https://aclanthology.org/2022.acl-long.469/) was presented at ACL 2022 main conference.

## ParaDetox Collection Pipeline

The ParaDetox Dataset collection was done via [Yandex.Toloka](https://toloka.yandex.com/) crowdsource platform. The collection was done in three steps:
* *Task 1:* **Generation of Paraphrases**: The first crowdsourcing task asks users to eliminate toxicity in a given sentence while keeping the content.
* *Task 2:* **Content Preservation Check**:  We show users the generated paraphrases along with their original variants and ask them to indicate if they have close meanings.
* *Task 3:* **Toxicity Check**: Finally, we check if the workers succeeded in removing toxicity.

Specifically this repo contains the results of **Task 1: Generation of Paraphrases**. The general size of the dataset is about 12,059 samples. Here, the samples that were marked by annotators that they cannot detoxify are present.
The reason for this can be following:
* *non-toxic*: the text is simply non toxic, can be with negative sentiment, however, without any obscene or rude lexicon;
* *toxic content*: the text is passive aggressive, sarcastic, or other, so the insult is deeply incorporated in the message. To detoxify it, you need to change the meaning dramantically.
* *unclear*: the text is only about obscene lexicon, random words, or any other tokens combination that makes it difficult to understand the main content.

Annotators could select several options.

## Citation

```
@inproceedings{logacheva-etal-2022-paradetox,
    title = "{P}ara{D}etox: Detoxification with Parallel Data",
    author = "Logacheva, Varvara  and
      Dementieva, Daryna  and
      Ustyantsev, Sergey  and
      Moskovskiy, Daniil  and
      Dale, David  and
      Krotova, Irina  and
      Semenov, Nikita  and
      Panchenko, Alexander",
    booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = may,
    year = "2022",
    address = "Dublin, Ireland",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.acl-long.469",
    pages = "6804--6818",
    abstract = "We present a novel pipeline for the collection of parallel data for the detoxification task. We collect non-toxic paraphrases for over 10,000 English toxic sentences. We also show that this pipeline can be used to distill a large existing corpus of paraphrases to get toxic-neutral sentence pairs. We release two parallel corpora which can be used for the training of detoxification models. To the best of our knowledge, these are the first parallel datasets for this task.We describe our pipeline in detail to make it fast to set up for a new language or domain, thus contributing to faster and easier development of new parallel resources.We train several detoxification models on the collected data and compare them with several baselines and state-of-the-art unsupervised approaches. We conduct both automatic and manual evaluations. All models trained on parallel data outperform the state-of-the-art unsupervised models by a large margin. This suggests that our novel datasets can boost the performance of detoxification systems.",
}
```

## Contacts

For any questions, please contact: Daryna Dementieva (dardem96@gmail.com)