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Natural Language Statistical Features of LSTM-Generated Texts.

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    Long short-term memory (LSTM) networks generate text that mimics human language, particularly in long-range correlations. An optimal generation parameter brings LSTM texts closest to natural language statistics.

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    Area of Science:

    • Natural Language Processing
    • Computational Linguistics
    • Artificial Intelligence

    Background:

    • Long short-term memory (LSTM) networks excel at natural language generation tasks.
    • Quantitative analysis of LSTM-generated text resemblance to human language is limited.

    Purpose of the Study:

    • To quantitatively compare the statistical structure of LSTM-generated text with human-written language.
    • To evaluate LSTM text generation against Markov models.

    Main Methods:

    • Characterized language structure using word-frequency statistics, long-range correlations, and entropy measures.
    • Compared statistical properties of texts generated by LSTMs, Markov models, and human writing.

    Main Results:

    • LSTM and Markov models replicate word-frequency and entropy measures found in natural language.
    • LSTM-generated texts uniquely reproduce long-range correlations comparable to human language.
    • An optimal 'temperature' parameter was identified for LSTM networks, yielding texts closest to natural language.

    Conclusions:

    • LSTM networks demonstrate a superior ability to capture complex statistical properties of natural language, especially long-range correlations.
    • The identified optimal parameter offers a method for enhancing the naturalness of LSTM-generated text.