Civil Rephrases Of Toxic Texts With Self-Supervised Transformers

By Leo Laugier, John Pavlopoulos, Jeffrey Scott Sorensen, Lucas Dixon. 2021

58 citations recorded by the source corpus.

Platforms that support online commentary, from social networks to news sites, are increasingly leveraging machine learning to assist their moderation efforts. But this process does not typically provide feedback to the author that would help them contribute according to the community guidelines. This is prohibitively time-consuming for human moderators to do, and computational approaches are still nascent. This work focuses on models that can help suggest rephrasings of toxic comments in a more civil manner. Inspired by recent progress in unpaired sequence-to-sequence tasks, a self-supervised learning model is introduced, called CAE-T5. CAE-T5 employs a pre-trained text-to-text transformer, which is fine tuned with a denoising and cyclic auto-encoder loss. Experimenting with the largest toxicity detection dataset to date (Civil Comments) our model generates sentences that are more fluent and better at preserving the initial content compared to earlier text style transfer systems which we compare with using several scoring systems and human evaluation.

Subfields: Artificial Intelligence, Molecular Biology, Natural Language Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Molecular Biology

In Natural Language Processing

Citations in the corpus

Challenges in Detoxifying Language Models (2021, background): “Other work on toxicity in generated text includes Xu et al. (2020), who investigate safety specifically in a dialogue setting, and translating existing offensive text into non-offensive variants (Nogueira dos Santos et al., 2018; Laugier et al., 2021).”
Towards Safer Generative Language Models: A Survey on Safety Risks, Evaluations, and Improvements (2023, background): “Similarly, text style transfer or rephrasing from toxicity to non-toxicity can also be plugged in this stage (Dale et al., 2021; Laugier et al., 2021).”
Text Detoxification using Large Pre-trained Neural Models (2021, methodology): “Finally, Laugier et al. (2021) detoxify sentences by fine-tuning T5 as a denoising autoencoder with additional cycle-consistency loss.”