ianwu13/marcodetoxification

Repository for "Detoxification with MaRCo: Controllable Revision with Experts and Anti-Experts"

MaRCo Detoxification

This is the repository for the 2023 ACL Paper "Detoxifying Text with MaRCo: Controllable Revision with Experts and Anti-Experts"

Using this Repository

Setting up the Environment

To set up the environment to run the code, make sure to have conda installed, then run

conda env create -f environment.yml

Then, activate the environment

conda activate rewrite

Important!: The environment is setup to run with a CUDA version compatible with RTX6000 GPUs. You may need to update the environment based on your own GPUs.

Compute Requirements

We recommend using a single RTX6000 GPU (this is what we used for our experiments) or another NVIDIA GPU with >24GB VRAM to enable large-scale rewriting (large batch size). Our method can also run on smaller models <24GB VRAM, but you should set the batch size to be lower.

Detoxification with MaRCo

See rewrite/README.md for details on how to run the detoxification pipeline,

Expert Models

The expert and anti-expert models are available on huggingface here:

• Expert: BART-base further finetuned on the non-toxic portion of the Jigsaw Corpus with the same pretraining masked denoising objective
• Anti-Expert: BART-base further finetuned on the toxic portion of the Jigsaw Corpus with the same pretraining masked denoising objective

You can download then load them or use these directly from the huggingface transfomers library, ie,

# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("hallisky/bart-base-nontoxic-expert")
model = AutoModelForSeq2SeqLM.from_pretrained("hallisky/bart-base-nontoxic-expert")

If you want to train your own expert/anti-expert models on BART with custom hyperparameter/dataset, please see the training folder of this repository.

Datasets

See datasets/README.md for access to the datasets and a description.

Evaluation

See evaluation/README.md for the evaluation pipeline on detoxifications.

Citing this Work

If you use/reference this work, please cite us with:

@inproceedings{hallinan-etal-2023-detoxifying,
    title = "Detoxifying Text with {M}a{RC}o: Controllable Revision with Experts and Anti-Experts",
    author = "Hallinan, Skyler  and
      Liu, Alisa  and
      Choi, Yejin  and
      Sap, Maarten",
    booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.acl-short.21",
    doi = "10.18653/v1/2023.acl-short.21",
    pages = "228--242",
    abstract = "Text detoxification has the potential to mitigate the harms of toxicity by rephrasing text to remove offensive meaning, but subtle toxicity remains challenging to tackle. We introduce MaRCo, a detoxification algorithm that combines controllable generation and text rewriting methods using a Product of Experts with autoencoder language models (LMs). MaRCo uses likelihoods under a non-toxic LM (expert) and a toxic LM (anti-expert) to find candidate words to mask and potentially replace. We evaluate our method on several subtle toxicity and microaggressions datasets, and show that it not only outperforms baselines on automatic metrics, but MaRCo{'}s rewrites are preferred 2.1 times more in human evaluation. Its applicability to instances of subtle toxicity is especially promising, demonstrating a path forward for addressing increasingly elusive online hate.",
}

Contact

If you have any issues with the repository, questions about the paper, or anything else, please email [email protected].