Before revision:  a policy gradient reinforcement learning technique uses that reward to fine-tune the language model weights. Using this fine-tuning technique , with 24,000 sentences from a science fiction plot summary dataset, halves the percentage of generated text containing non-normative behavior from 35.1\%to 15.7\% .
After revision:  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 behavior from 35.1\%to 15.7\% .