  Artificial writing is permeating our lives due to recent advances in
large-scale, transformer-based language models (LMs) such as BERT, its
variants, GPT-2/3, and others. Using them as pretrained models and fine-tuning
them for specific tasks, researchers have extended the state of the art for
many NLP tasks and shown that they not only capture linguistic knowledge but
also retain general knowledge implicitly present in the data. These and other
successes are exciting. Unfortunately, LMs trained on unfiltered text corpora
suffer from degenerate and biased behaviour. While this is well established, we
show that recent improvements of LMs also store ethical and moral values of the
society and actually bring a ``moral dimension'' to surface: the values are
capture geometrically by a direction in the embedding space, reflecting well
the agreement of phrases to social norms implicitly expressed in the training
texts. This provides a path for attenuating or even preventing toxic
degeneration in LMs. Since one can now rate the (non-)normativity of arbitrary
phrases without explicitly training the LM for this task, the moral dimension
can be used as ``moral compass'' guiding (even other) LMs towards producing
normative text, as we will show.
