Automated Korean poetry generation using LSTM autoencoder

초록

Automatically composing poem is considered a highly challenging task, and it has received increasing attention across various fields. The computer has to ensure readability, meaningfulness, and vocabulary adequacy as well as the semantics of the poet's production, which are otherwise realized by their imagination and inspiration. In this study, a model for Korean poem generation based on long short-term memory (LSTM) is proposed with the aim of creating poems that imitate the writing style of four poets who represent Korea's modern poetry. To do this, 1000 poems by the target poets were collected, and their styles were defined using natural language processing (NLP). Following this, each sentence of the poem was preprocessed, and training was performed using LSTM. When a user selects the desired poet and enters a keyword, the model automatically generates a poem based on that poet's style. The poems produced showed some errors in syntactic structure and semantic delivery, but they successfully reproduced the characteristic vocabulary and emotions of the poet. Copyright © by the paper's authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

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