Human Perception in Natural Language Generation

Abstract

We take a collection of short texts, some of which are human-written, while others are automatically generated, and ask subjects, who are unaware of the texts’ source, whether they perceive them as human-produced. We use this data to fine-tune a GPT-2 model to push it to generate more human-like texts, and observe that the production of this fine-tuned model is indeed perceived as more human-like than that of the original model. Contextually, we show that our automatic evaluation strategy correlates well with human judgements. We also run a linguistic analysis to unveil the characteristics of human- vs machine-perceived language.