  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.
