  Large-scale, transformer-based language models such as GPT-2 are pretrained
on diverse corpora scraped from the internet. Consequently, they are prone to
generating non-normative text (i.e. in violation of social norms). We introduce
a technique for fine-tuning GPT-2, using a policy gradient reinforcement
learning technique and a normative text classifier to produce reward and
punishment values. We evaluate our technique on five data sets using automated
and human participant experiments. The normative text classifier is 81-90%
accurate when compared to gold-standard human judgments of normative and
non-normative generated text. Our normative fine-tuning technique is able to
reduce non-normative text by 27-61%, depending on the data set.
