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Automatic evaluation of various text quality criteria produced by data-drivenintelligent methods is very common and useful because it is cheap, fast, andusually yields repeatable results. In this paper, we present an attempt toautomate the human likeliness evaluation of the output text samples coming fromnatural language generation methods used to solve several tasks. We propose touse a human likeliness score that shows the percentage of the output samplesfrom a method that look as if they were written by a human. Instead of havinghuman participants label or rate those samples, we completely automate theprocess by using a discrimination procedure based on large pretrained languagemodels and their probability distributions. As follow up, we plan to perform anempirical analysis of human-written and machine-generated texts to find theoptimal setup of this evaluation approach. A validation procedure involvinghuman participants will also check how the automatic evaluation correlates withhuman judgments.

Human or Machine: Automating Human Likeliness Evaluation of NLG Texts