Natural Language Statistical Features of LSTM-Generated Texts

Marco Lippi, Marcelo A. Montemurro, Mirko Degli Esposti, Giampaolo Cristadoro
2019 IEEE Transactions on Neural Networks and Learning Systems  
Long short-term memory (LSTM) networks have recently shown remarkable performance in several tasks that are dealing with natural language generation, such as image captioning or poetry composition. Yet, only few works have analyzed text generated by LSTMs in order to quantitatively evaluate to which extent such artificial texts resemble those generated by humans. We compared the statistical structure of LSTM-generated language to that of written natural language, and to those produced by Markov
more » ... models of various orders. In particular, we characterized the statistical structure of language by assessing word-frequency statistics, long-range correlations, and entropy measures. Our main finding is that while both LSTM- and Markov-generated texts can exhibit features similar to real ones in their word-frequency statistics and entropy measures, LSTM-texts are shown to reproduce long-range correlations at scales comparable to those found in natural language. Moreover, for LSTM networks, a temperature-like parameter controlling the generation process shows an optimal value-for which the produced texts are closest to real language-consistent across different statistical features investigated.
doi:10.1109/tnnls.2019.2890970 pmid:30951479 fatcat:arxskczkcvgadn67wqksxe3tjq