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.
The question of whether a computer is capable of writing text with creative features such as poetry (and if so, how it will differ from human creation) has become a popular one among researchers in the fields of natural language generation (NLG), computational creativity, and, broadly, artificial intelligence (AI). While a significant number of studies have long attempted to achieve automatic responses to a series of questions, automatic poetry generation has been receiving increasing attention in recent times. This is a challenging research area, because it requires a very high level of skill to satisfy the formal conditions and content of the poem.
Automated poetry creation not only indicates technological progress, but it represents
a new creative approach altogether that is entirely different from the existing concept and
principles of poetry creation. The shift in the perception of poetry creation methods dates
back to 1920, even before computers were accessible. It can be observed in a poem by
Tristan Tzara, a poet who participated in a new European art movement called Dadaism
in the early 20th century. His poem [
Take a newspaper.
Take a pair of scissors.
Choose an article as long as you are planning to make your poem.
Cut out the article.
Then cut out each of the words that make up this article and put them in a bag. Shake it gently.
Then take out the scraps one after the other in the order in which they left the bag.
Copy conscientiously.
The poem will be like you.
And here are you a writer, infinitely original and endowed with a sensibility that is charming though beyond the understanding of the vulgar.
Zara’s poem, published in 1920, proposed a complete departure from traditional poetry. In it, the poet finds adequate poetic words and combines them according to the rules; their intentions, feelings, or causality cannot be found. This recalls the definition of an algorithm, meaning a procedure or method for solving a problem or a step for performing a task.
With the advent of computers, experimental attempts were made to generate poems.
Bailey [
The use of deep learning algorithms such as recurrent neural networks (RNNs) and LSTM in computational creativity has evolved the concept of automatic poetry generation. In this context, the present study proposes a Korean poetry generation model based on deep learning which aims to imitate the writing style of a particular poet.
First, four poets, Kim So Wol, Yoon Dong-Joo, Baek Seok, and Jeong Ji-yong, who represent Korea’s modern poetry and remain popular amongst Koreans, were selected. These poets wrote noteworthy poems within the forms of free poetry and lyric poetry during the Japanese occupation.
The rests of the paper are organized as follows. We start by reviewing previous works in the Section 2. Section 3 describes the approach adapted in our experiments. And we illustrate the evaluation in Section 4. The conclusion of this paper and future work are demonstrated in Section 5. 2
If Bailey [
Recently, along with the advance of machine learning, poetry generation using deep
learning have emerged. Wang et al. [
Poetic generation researches in various languages such as English, Japanese, Chinese,
and Italian are being actively conducted. Regarding Korean poem generation, Park et al.
[
The whole experiment workflow is shown in Figure 1. At the beginning of the experiment, we first collected 400 poems written by four poets. The poetic work of the target authors is usually not enough to successfully train deep neural networks, so we collected a total of 1000 pieces by adding other poems written during the Japanese colonial period. Before training the data using LSTM, we removed old language, Chinese character, etc. from the poem in the pre-processing process and then numbered each line of the poem. When a user enters a keyword and clicks a poet, a poem is generated. 3.2
We attempted a quantitative analysis of poetry text to define the style of each poet. To do this, we extracted high frequency vocabularies using part-of-speech (POS) tagging and made a cloud composed of top frequent word as shown in Figure 2. Besides, we analyzed the co-occurrence patterns of words through bi-gram.
From the results of the stylistic analysis, certain characteristics could be determined. First, vocabulary related to nature appeared frequently in the poems by the specified poets; second, the primary emotion influencing their poetry is sorrow; and third, sensory expressions representing nature are often used. Finally, the use of first-person pronouns ‘I’ and determiner such as ‘this’ was high. Table 1 shows the results of the stylistic analysis.
Determiner
Authors Examples $Ùü (Yun Dong-Joo) $ (night), X (sky), Ä (star), etc.
À© (Jeong Ji-yong) ä (sea), < (water), etc. @Ô (Kim So Wol) ° (mountain), 4 (tree), etc. 1 (Baek Suk) È (bird), l¬ (frog), etc. ¬ (sad), xm (alone), 4m (painful), ý (sorrow), etc. @ (red), xx (blue), @ (bright), @ (black), @ (high), (p ´ (hot), etc. (I) t(this), ø(that), etc.
Table 1: The results of the stylistic analysis. We present an approach based on LSTM to generate Korean poems with a specific style. As shown in Figure 3 and 4, we constructed a word-level encoder-decoder LSTM network, entered the author’s name, poem title, the line number of the poem, and trained to minimize the difference between the target sentence and the sentence generated by the network.
When the sequence (author name, poem title, poem’s line number) enters the encoder network, this network performs word embedding and inputs it to the LSTM network to extract the feature values of the input sequence. The decoder network applies Attention Mechanism to train the relationship between input sequences and output sentences. Hidden size of each network is 256 Dimension and depth of layer is 3. The maximum length that can be trained is given by 30-word sequence length. All LSTM networks were initialized to zero before training.
Some researchers have suggested criteria for evaluating automatically composed texts.
For example, Manurung et al. [
In the present study, readability, meaningfulness, and grammaticality were adopted, and five evaluators chose 60 poems out of 1000 based on the three criteria. However, some errors were found in the chosen poems, the most prominent of which were syntactic and semantic errors. In Korean, adjectives are generally placed before nouns. However, sentences that violate such syntactic rules were found. Additionally, in some cases, sentence meanings could not be gathered despite the absence of syntactic errors. In order to improve on these aspects, a significant amount of further study is required.
When a user clicks on one of the four poets and enters the desired keyword, a new poem reflecting the poet’s style is created. Even if the same keyword is entered repeatedly, new results will be generated each time. The reproductions of the poets’ vocabulary and emotions were analyzed in 60 poems. Words related to nature, such as rivers, skies, mountains, and the sea appeared in the title and content of the poem, and emotional words representing sorrow and loneliness were used. However, some meaninglessly repeated words affected the readability of the poems. 5
Automatic poetry composition is a highly challenging problem because poetry is a genre of literature that expresses human imagination and creativity. Several studies have been conducted to generate poems in various languages such as English, Chinese, and
Japanese. In this paper, an LSTM-based approach to produce a Korean poem with a specific style is presented. When a user enters a keyword and clicks on a poet, the model creates a poem that reflects the poet’s writing style. Korean poem generation research is just beginning; this work is expected to contribute to Korean text generation research.
This research was supported by the Korea Creative Content Agency, under the Ministry of Culture, Sports and Tourism.
You et al.