Discrimination of human-written and human and machine written sentences using text consistency

The development of deep learning has made it possible to automatically generate sentences that could be misinterpreted as being written by humans in the field of natural language processing. As a result, the importance of the identity of the author of the text is beginning to be emphasized. In this paper, we propose a method to evaluate the consistency of sentences, which can distinguish between "sentences composed entirely of human-written texts" and "sentences with a mixture of human-written and machine-generated texts". In addition, we tested the consistency of the proposed method in an experiment, and confirmed that it was possible to discriminate two kinds of sentences in a mixed dataset of human written text and mixed text with higher accuracy than existing works. Furthermore, Kendall's rank correlation coefficient and Mann-Whitney's U-test in the sentence discrimination experiment confirmed that the proposed method showed a significant difference between the two types of sentences with a stronger correlation.