Transformer-based Language Models (LMs) achieve remarkable performances on a
variety of NLU tasks, but are also prone to generating toxic texts such as
insults, threats, and profanities which limit their adaptations to the
real-world applications. To overcome this issue, a few text generation
approaches aim to detoxify toxic texts with additional LMs or perturbations.
However, previous methods require excessive memory, computations, and time
which are serious bottlenecks in their real-world application. To address such
limitations, we propose an effective yet efficient method for language
detoxification using an attribute-discriminative latent space. Specifically, we
project the latent space of an original Transformer LM to a discriminative
latent space on which the texts are well-separated by their attributes, with
the help of a projection block and a discriminator. This allows the LM to
control the text generation to be non-toxic with minimal memory and computation
overhead. We validate our model, Attribute-Discriminative Language Model (ADLM)
on detoxified language and dialogue generation tasks, on which our method
significantly outperforms baselines both in performance and efficiency.

此研究提出了使用属性鑑别潜在空間进行语言去毒化的方法，通过投影技术和鑑别器帮助文本被其属性分离，以实现最小内存和计算开销的非毒性文本生成，取得了比基线更优秀的性能和效率。