# Creative Data Generation: A Review Focusing on Text and Poetry

Mohamad Elzohbi, Richard Zhao
Department of Computer Science, University of Calgary
Calgary, Alberta, Canada T2N 1N4
{melzohbi<EMAIL_ADDRESS>
###### Abstract

> The rapid advancement in machine learning has led to a surge in automatic
> data generation, making it increasingly challenging to differentiate between
> naturally or human-generated data and machine-generated data. Despite these
> advancements, the generation of creative data remains a challenge. This
> paper aims to investigate and comprehend the essence of creativity, both in
> general and within the context of natural language generation. We review
> various approaches to creative writing devices and tasks, with a specific
> focus on the generation of poetry. We aim to shed light on the challenges
> and opportunities in the field of creative data generation.

## Introduction

Data refers to information that can be stored and processed by a computer. It
can take many forms and can be generated both naturally, such as through the
thoughts and ideas in a person’s mind, and artificially, such as through the
use of machine-based models. In particular, computer scientists use the term
data to refer to anything that can be stored in the computer’s memory called
binary data. The process of generating data can be (1) fully automated, like
using a generative model to generate poetry from topic words, (2) semi-
automated, where the output is a collaboration between the machine and the
human, such as a poem draft generated with human refinements (?), or (3)
entirely manual such as using a text editor to write a poem. The concept of
creativity has historically been difficult to define and has not been
considered seriously in AI, as it was thought that machines need first to be
capable of possessing thoughts and experiencing emotions (?). With the advent
of deep learning and transformer models, natural language generation (NLG)
techniques have become more advanced and the possibility of generating
creative output seems more viable.

This paper focuses on the intersection between creative data generation and
NLG with an emphasis on poetry generation. First, we address the difficulties
in defining creativity and present our own perspective on the key elements of
creativity. Second, we review relevant metrics used and how they are relevant
to the proposed criteria, then we evaluate NLG models based on those criteria.
Third, we provide an overview of the practical creative applications of text
generation tasks, reviewing some of the most recent work in this area. We
focus on the poetry generation task, examining the methods and models
employed.

## Creativity Paradox

Creativity is a complex and often-debated concept that is far from being well-
defined. While many tend to focus on the creativity of the end result, the
process by which it is created may also play a role in determining its level
of creativity. When the steps involved in generating an output are clearly
defined and easy to replicate, the output may be seen as less creative, even
if it was initially considered as such. For example, the creativity of
Johannes Vermeer, one of the most well-known Dutch artists in western history,
was put onto the table of controversial discussions when Philip Steadman
suggested that Vermeer was using optics in the painting process (?; ?).
Furthermore, the English painter ? (?) argued that most painters since the
15th century secretly used their knowledge of optical science in the painting
process. It could be questioned as to why certain painters would choose to
keep their techniques secret if this does not raise doubts about the
authenticity of their artistic talent and creativity. One possible explanation
is that artists desire for their work to be perceived as unique and
distinguished from craft-based forms of expression. They may view their work
as constituting a higher form of artistic expression, and therefore seek
recognition as elite artists rather than as craftsmen, who are considered to
have a lower social status (?). This indicates that the level of creativity in
the output can be significantly impacted by the degree of unpredictability.
Hence, can photography be considered creative? Paul Delaroche, a classical
painter, believed that the emergence of photography marked the death of
painting. However, painters disagree and see painting as expressive and
original, as it is not simply a mirror reflection of the real world. (?). On
the other side, pictorialists argue that they can express their vision through
photography by adding their own touches to a real photograph (?).
Consequently, how valid is it to attribute creativity to machines if they are
simply following a predetermined set of rules written by humans? In fact, even
when we have a clear understanding of the design of a machine, we may not
fully comprehend the internal processes that occur during its training. Does
the hint of anonymity allow for the possibility of attributing creativity to
them? Does revealing the process diminish the perceived originality of its
output? Do we, as humans, understand creativity based on beautifulness or
wonderfulness of the output?

? (?) summarizes the creative process into three main schools: (1)
structuralists: who outline the creative process in four stages: (i) gathering
information (preparation), (ii) forming new connections (incubation), (iii)
finding sudden insights (illumination) and (iv) verifying and refining those
insights (verification). (2) inspirationalists: who believe that creative
insights can be achieved through sketching, visualization and meditation. (3)
situationalists: who see creativity as a social process. In ? (?)’s point of
view, creativity must induce (i) new, (ii) surprising, and (iii) valuable
ideas or artifacts. As a situationalist, ? (?) asserts that novelty is not
enough to be considered creative, but the work must be accepted by the
relevant field. ? (?) argues that machines are merely tools in the hands of
artists. These tools are not always predictable but not inherently creative. ?
argues that just as we do not attribute creativity to a brush when watercolors
flow on a canvas or to nature and animals when they form beautiful patterns
and stunning structures, although animals have brains just like humans, we
should not attribute it to machines.

Ultimately, the concept of creativity appears to be complex and multifaceted,
making it seem impossible to develop a cohesive paradigm. However, we can see
recurring themes and intersecting dimensions that we will summarize in the
following: (see Figure 1)

  1. 1.

Originality: To be considered creative, an output must strike a balance in
originality. To achieve this, a creative model must avoid lacking originality
(plagiarism) especially when learning from creative works. The greater the
originality in the output, the more creative it is considered to be. However,
too much originality results in confusion, disconnection, and excessive
refinement (preciosity).

  2. 2.

Unpredictability: An output that is obvious and predictable is generally seen
as banal and boring. The greater the surprise factor in the output, the more
creative it is considered to be. However, the output that is completely
unpredictable may be perceived as nonsensical and lose its creativity. To
achieve a balance between surprise and coherence, a creative model may need to
learn a human-like probability distribution and optimize its decoding
strategies for creative expression.

  3. 3.

Sociability: We define sociability as the density of meaning and imagery in
the output in relation to its appropriate domain. Poets and painters, for
example, use language and colors, respectively, to compact complex ideas and
emotions into a limited space, such as a verse or a canvas. A work that is
semantically condensed provides more material for the artist or critic to
engage with and assess. If the work is less sociable, it may be seen as
randomly generated. However, it is important for the output to strike a
balance, as an output that is overly dense may be complex and difficult to
comprehend.

Figure 1: Creativity Dimensions.

## Creative NLG: Aspects

Discussions about NLG in the past have centered around determining what
constitutes as real NLG (?). As the field progresses, the focus has shifted
towards determining what constitutes as creative NLG. We believe that a
creative NLG model must exhibit a sufficient level of originality,
sociability, and unpredictability.

Despite the significant advances in NLG, the study of the originality in
language models has received relatively little attention. Originality in
language can take various forms, such as phrasal, thematic, semantic, or
stylistic originality, among others. Although probabilistic models may lead to
the generation of original phrases, there is also the risk of over-fitting and
copying from the training corpus. Plagiarism in creative language is
particularly noticeable, as the output deviates from the normal use of
language. This has been, recently, a concern in other creative fields, such as
in music generation (?). ? (?) have noted that there is currently no standard
automatic test for originality in NLG and proposed an approach to test the
original use of language to identify plagiarism violations using a phrasal
counting approach in the ground truth. However, this approach is limited to
measuring the phrasal originality which is also important to identify phrases
that diverted from their connotation and lost their imagery due to repetitive
use in ordinary speech, such as dead metaphors.

? (?) conducted experiments to predict language redundancy by asking humans to
guess the next character in an English passage. ? assumed that humans sample
from a probability distribution based on their previously learned letter-
sequence probabilities and introduced the concept of language entropy.

$H=-\sum\nolimits_{{i}}{p\left({{w_{i}}}\right)\
\log\left({p\left({{w_{i}}}\right)}\right)}$

where $p(w_{i})$ is the probability for the $i^{th}$ n-gram to occur. ? (?)
studied the effect of several factors, including structure, on the redundancy
of the language and found that prose is more redundant than poetic language. A
more redundant language is less dense hence less sociable. Due to form
restrictions in poems, it seems at first glance that the population of the
allowed text must decrease from that of prose, but ? (?) postulates that
poetic devices expand the population due to the relaxation of language norms.
A well-known metric that is used to test NLG output quality is perplexity,
which is directly related to entropy. A study on the correspondence between
perplexity and human evaluation revealed that creative output requires
perplexity that is not too high and not too low (?).

A recent study (?) has introduced the concept of active divergence, which
refers to the ability of a model to actively diverge from the training data to
achieve a more original and diverse output, as opposed to the traditional
method of training the model to perfectly mimic the training data. However, it
may be more challenging for a language model to diverge from the training data
while still adhering to the rules of grammar, semantics, and syntax. The
sampling methods also play an essential role in the output predictability.
Applying random sampling leads to a random non-sensical output, and greedy
decoding tends to prefer banal or repetitive output. ? (?) noted that beam
search decoding in NLG models may result in less surprising and more monotonic
output compared to the high variability in human choice of words. In order to
increase unpredictability while keeping randomness low, they proposed Nucleus
Sampling, which randomly sample from the top-$k$ most probable words and set a
probability threshold $p$ to ensure that sampling does not occur from peaked
or flat distributions. Only the top-$p$ tokens are considered where $V^{(p)}$
is the smallest set of tokens such that:

$\sum\nolimits_{{x\in V^{(p)}}}{P\left({{x|x_{1:i-1}}}\right)\geq{{p}}}$

The level of creativity in language models is influenced not only by the
inference algorithms, but also by how similar the machine language modeling is
to human language modeling. Deep learning models, particularly those that use
transfer learning, have achieved impressive results on NLG tasks. Auto-
regressive (AR) models, such as Generative Pre-Training models (GPT/2/3),
generate text by sampling the next token from a probability distribution.
Although humans may use causal language when they speak, they often try to
modify their speech when they attempt to write creative text. Masked Language
Models (MLM), such as BERT, predict masked tokens in a sentence based on
context, which is similar to how humans try to guess a better word after
removing it from a sentence and considering the context. However, these models
will not automatically identify the words to remove and replace. Guided
generation allows the model to be responsive and adaptable, and to accept or
reject criteria, which may be a step towards a more social model. A study on
the steerability of Generative Adversarial Networks (GANs) (?) shows that they
can extrapolate yet are limited to the diversity of the data set. Previous NLG
models have been limited by fixed or monotonically increasing sequence
lengths, but Levenshtein Transformer (?) and Edit-based Transformer (?) allow
for dynamic length changes. However, current large language models (LLMs) tend
to overlook the integration of the unique elements associated with various
forms of creative writing, like poetic diction, in their training process.

## Creative NLG: Devices and Tasks

As discussed earlier, creativity in natural language appears in the original,
rich and unusual use of language. Figurative language utilizes figures of
speech to modify the literal meanings of words and produce imagery, or
utilizes figures of sound to produce an appealing form. These rhetoric devices
are used in creative writing in special ways to ensure novelty, richness and
unpredictability. This includes similes, metaphors, hyperboles, sarcasm,
humor, rhythms and rhymes, among others. These techniques are combined to
shape the elements of various types of creative writing such as poetry and
prose novels. While most research has focused on the detection of these
literary devices, it was until recently that researchers showed interest in
models that generate such devices. The employment of MLM and AR based models,
along with the versatile BART model that amalgamates the strengths of both
approaches and the use of COMET (?), designed for generating commonsense
knowledge, underpinned the observed trend. Next, we will review recent work
and its impact on downstream NLG tasks before we introduce poetry generation.

#### Metaphor:

Automated metaphor generation has only recently received increased attention
(?). MERMAID (?) fine-tunes BART with an automatically generated parallel
corpus using an MLM model. The MLM will replace metaphorical verbs with their
literal complement using the symbol relation from COMET. Then, they use a
generator-discriminator approach to transform literal expressions to
metaphorical ones with a verb-replacement objective and a top-$k$ sampling
strategy. The generated sentences are scored using a RoBERTa-based metaphor
detection model to favor sentences with higher quality metaphorical verbs. ?
(?) propose a similar model that replaces literal verbs with metaphorical ones
guided by conceptual mapping. The authors propose two methods, one by training
a frame embedding model and the other by finetuning BART using a generated
parallel corpus similar to the one used in MERMAID but tagging each sentence
with FrameNet frame labels. A limitation of these models is that they only
generate metaphorical verbs and assume a given context.

#### Simile:

? (?) finetune BART on an automatically built corpus that transforms similes
to their literal compliments using the “has property” relationship provided by
COMET to generate similes from a given literal sentence by replacing a word by
a novel simile. A limitation of the proposed model is that it explicitly
replaces only an adjective or an adverb from a given input. ? (?) propose a
simile insertion approach based on the general context of the sentence without
replacing words. The approach consists of two main stages: (1) detecting an
appropriate simile insertion position in the input sentence using a BERT-based
model (2) generating a simile at that position using a standard transformer
decoder. Both models are trained jointly in a multi-task learning setup.

#### Hyperbole:

MOVER (?) finetunes BART on a dataset of hyperboles retrieved from an online
corpus using a BERT-based hyperbole detection model. The model is trained on a
mask-filling task where potential hyperbolic tokens are identified, masked,
and then regenerated based on their part-of-speech (POS) n-gram and ranked by
an unexpectedness score. To generate a hyperbole, the model masks a non-
hyperbole sentence span using a POS n-gram and then selects the highest-ranked
of multiple possible hyperbole candidates using a BERT-based hyperbole ranker.
HypoGen (?) focuses on clause-level hyperboles with a specific pattern of the
form (A1 is so A2 that B is D). To train this model, the authors collected and
annotated a dataset of hyperboles from Reddit posts (HYPO-Red), selecting a
subset that contains the target pattern and analyzing the relationships
between clauses. They then used COMET and reverse-COMET models to generate
candidate clauses with commonsense and counterfactual relations, respectively,
and used BERT-based and neural-based classifiers to select the top-$k$
candidates.

#### Sarcasm:

To generate sarcasm, ? (?) negate or replace evaluative words with their
antonyms and provide a context to emphasize the semantic incongruity between
the intended sarcasm and the context. Several context candidates are retrieved
using a concept derived from the COMET model and an online corpus. The
candidates are ranked by a RoBERTa model for semantic incongruity and the
highest scored are concatenated to the sarcastic sentence. ? (?) argue that it
is not sufficient to only negate the literal meaning of a sentence to produce
sarcasm and propose a framework that is based on the implicit display theory
of sarcasm to generate a sarcastic response to an input. The framework
identifies an ironic environment by negating the event in the input utterance.
A COMET-based rule-based method is used to produce an insincere negative
attitude response based on the input event and one of the following relations:
the action needed to perform the event, the attribute needed to perform the
action, the user reaction, and the effect of the action on the user.

#### Puns:

? (?) use a conditional LSTM model to generate homographic puns given two
senses of a pun word. They use a joint beam search decoding with a forward and
backward generation centered by the target pun word. Both sequences are then
concatenated to form the final pun sentence. AMBIPUN (?) generates homographic
pun sentences using GPT-3 to generate related context words given two
different senses of a pun word. The context words are then combined using a T5
model to generate candidate sentences and a BERT-based model to rank and pick
a final pun sentence.

#### Multi-Figurative:

Creative writing tasks need to employ various literary techniques in the
generation. It is more practical to have one multi-figurative generation model
to do so. ? (?) propose a multi-figurative model that can transform between
literal or figurative forms to another figurative form. The authors first
finetune a BART model on a denoising objective to infuse multi-figurative
sense into the model, then they fine-tune the model on a literal to figurative
paraphrasing objective with a parallel data corpus and another time with a
figurative to figurative paraphrasing objective with a cross attention layer
to leak the target figure of speech to the encoder. For inference, they either
transform the input text to the target figure of speech directly or to a
literal form first and then to the target figurative form.

### Impact on NLG Downstream Tasks

Generating creative language plays an essential role in many text-to-text NLG
tasks, especially in creative writing tasks such as poetry and prose novels or
stories. It also improves the quality of downstream tasks such as machine
translation, dialogue generation, text summarization, style transfer, and even
other data-to-text applications.

#### Storytelling

is to generate an open-ended text that conveys to the reader a comprehensive
story. It is one of the creative writing tasks written in prose form as
opposed to poetry. Storytelling needs to account for soft and hard constraints
such as the topics, plots and characters among other aspects to keep the story
consistent and coherent. AI-aided approaches were used (?) to collaboratively
write and plan story plots. Plug-and-Play Language Model (PPLM) (?) is used
for controllable story generation by plugging an attribute classifier on top
of a GPT-2 transformer that checks how similar the next token is to a given
topic. The classifier will be exposed to the history of latent representations
of the generated words, then repeatedly perform backward and forward passes
through the classifier and the generator calculating gradients each time,
updating the representations, and handing them back to the generator. This
will steer the generation process towards the topics at inference time with
minimal training. The work was limited to assistive stories generation,
completing a story skeleton given a story theme or sentiment. ? (?) introduce
multiple emotion-aware GPT-2-based models coupled with the COMET model to
generate stories given a title and an emotional arc of the protagonist. Their
work was the first to generate stories with an emotional trajectory plan.

#### Text Style Transfer

is gaining an increasing popularity in natural language generation (?).
Variational Auto-Encoders (VUAs) were used to rewrite modern text in
Shakespeare style (?). ? (?) used a transformer-based model (XLNet) to
generate a parody of lyrics, changing the lyrics while preserving the rhythm
and syllable count.

#### Machine Translation

? (?) examines the challenges and strategies involved in translating jokes and
puns. The author argues that while some forms of humor are easily
translatable, others, such as those that rely on wordplay and cultural
references, can be particularly difficult. To effectively translate this kind
of humor, the paper suggests using a variety of strategies, such as sense
transferring, semantic leaps and cultural substitution.

#### Dialogue Generation

can be modulated to generate creative dialogue content such as improv games,
battle rapping, interactional jokes and conversational narratives, among
others. In fact, recent chatbots such as LaMDA (?) and
ChatGPT111openai.com/blog/chatgpt/ have shown the ability to write and
complete jokes, poems, and stories in conversations.

#### Text Summarization

is the task of condensing a longer text preserving the most important
information within its content. Similarly, creative language generation can be
seen as summarizing broad ideas and beautiful imagery using words. In fact,
iPoet (?) formulates the poetry generation task as a text summarization task.
Given users’ intents, write a poem that obeys poem requirements by retrieving
terms out of a poetry corpora.

#### Data-to-text generation:

Creative data-to-text tasks are analogous to how humans describe, in creative
writing, what they see or hear from natural beauty, scenery, natural
soundscape, etc. ? (?) used Inception, ConceptNet and LSTM models to generate
image-inspired poems. ? (?) proposed a multi-adversarial CNN-RNN-based GAN
model to caption images with a poem. ? (?) proposed transformer-based encoder
and decoder models to generate emotional narratives from visual embedding
extracted from images using CNNs. ? (?) demonstrated the power of utilizing
their explained emotion-captioned image data set in enhancing language models
to express and explain emotions triggered by artistic images. In addition, ?
(?) proposed a SeqGAN-based model that generates a matching line of lyrics
with an input melody.

## Poetry Generation

Modeling poetry is more difficult than language modeling as it requires the
machine not only to understand but also to use language as a creative tool. As
previously discussed, creativity is not yet well-defined, and it is uncertain
if computers can achieve it.

#### Definition

Before discussing how to generate poetry, we first need to define poetry,
which is equally difficult as defining creativity itself. In fact, there are
as many ways to describe poetry as there are people (?). Poetry is often
defined as a type of writing that uses distinctive style, rhythm, and language
to convey intense feelings and ideas 222 oed.com/view/Entry/146552 or the
writing that concentrates imaginative experience to elicit a specific
emotional response through meaning, rhythm, and sound patterns 333merriam-
webster.com/dictionary/poetry. According to ? (?), poetry is the writing that
violates the logical sequence and semantic categories of prose. Both prose and
poetry may use literary devices such as metaphors, similes, ironies, and puns
to express ideas and evoke emotions. Poetry, however, employs specific, but
not essential, devices such as rhyme, rhythm, and meter. Meanwhile, the most
notable devices found in a poem are its versification and form coherence.

#### Creativity Dimensions

Various definitions of poetry highlight the three dimensions of creativity
previously mentioned, delivered through language. A poem is a language
artifact that requires the use of language to express meaning, and thus must
adhere to linguistic rules to some extent while also allowing for violations,
commonly referred to as poetic license. Originality and unpredictability can
be observed through the surprising deviation from prose and normal speech
rules, as well as through the use of poetic devices and euphony. The social
aspect of poetry can be emphasized through the concentration of ideas, imagery
and emotions within the boundaries of verses and stanzas, as well as the
ability for the poem to be subject to criticism.

#### Forms

English poetry can be classified based on various poetic characteristics.
Notably, rhythm and rhyme schemes are often used to classify poems. The most
popular forms include: (1) Free Verse: which has no constraints on a specific
form. (2) Haiku: a short form of poetry that follows a 5-7-5 syllabic pattern.
(3) Sonnet: characterized by specific rhyme and meter schemes. Shakespearean
sonnet is considered one of the most well-known with 3 quatrains and a
couplet: ABAB CDCD EFEF GG rhyme scheme. (4) Blank Verse: consists of unrhymed
but metered verses. (5) Limerick: consists of five lines with seven to ten
syllables with a verbal rhythm and the first, second, and fifth lines rhyming.

#### Goals

The most common goals of writing poetry according to ? (?) are to imitate
reality, attain special effects on the readers, communicate emotions, and be
art for art’s sake. Similarly, the automatic poetry generation must represent
adequate computational goals. ? (?) upheld in an early view that the possible
usefulness of computer poetry is to influence the doer (designer/end-user) to
learn more about language, poets and poetry.

Figure 2: Poetry Generation Timeline.

### Poetry Generation Techniques

Automatic poetry generation was documented as early as 1959 with a word salad
approach by manually enhancing permutations of poem words (?). Since the few
similar early attempts, the automatic poetry generation topic was inactive
until the 1990s, where some works started getting attention in the field (?).
Early poetry generation techniques can be categorized (?) into: template-
based, generate-and-test, evolutionary, and case-based reasoning. ? (?) groups
poetry generation approaches into: word salad, template/grammar-based, form-
aware and poetry generation systems. ? argues that the first three methods do
not satisfy the definition of poetry generators being: aware of
meaningfulness, grammaticality and poeticness. ? (?) propose a generic
categorization of poetry generation methods that works on any creative task:
mere, human-enhanced, and computer-enhanced approaches. ? (?) reviews poetry
generation methods from multiple intersecting angles: languages, forms,
content quality, techniques, and evaluation. In addition, ? extends the list
of techniques with: chart generation, constraint satisfaction, multi-agent,
and language models such as Markov and RNN models. In this paper, we use a
different taxonomy based on the intersection of chronology and methodology,
pointing to the proposed creativity dimensions whenever applicable. We have
found that the development of poetry generation techniques starts with rule-
based, heuristic-based, statistical-based, then deep-learning-based approaches
that are roughly proposed in this chronological order (Fig. 2).

#### Rule-based Approaches

The early methods are based on following crafted rules and filling poem
templates. Templates are the poem shells where actual words are taken out from
original poems, while some of their characteristics, such as part of speech,
are preserved as placeholders to be systematically filled with other words
matching the characteristics. RETURNER (?) is one of the earliest programs
used to automatically generate English poems. The author designed three
versions of a template-based model to generate three poems. The first version
generates a poem given only the vocabulary of a real poem “Return by Alberta
Turner.” The second version was based on subjects and verbs with randomly
decided optional modifiers, complements, and end conjunctions to provide the
opportunity for iteration. The third version is based on a six-slot
grammatical matrix and a set of rules to guide the generation process. ? (?)
uses a form-aware chart-based generation to generate strings that satisfy
rhythm constraints. Meter validation is performed at each step of the process
to reduce the search space. WASP (?) fills Spanish poem templates based on
initial vocabulary and verse patterns, generating and validating verse drafts
at each iteration according to length, rhyme, and rhythm constraints. The main
focus of these early systems is on the poetic form rather than on the creative
language. ASPERA (?) is an evolved version of WASP with case-based reasoning
to retrieve an existing form and vocabulary that are highly similar to the
user-defined target specifications and then adapt to meet the desired
specifications. However, this model is not fully automated and needs to
manually interact with a human user to validate or correct the generated
draft.

#### Heuristic-based Approaches

Using rule-based approaches is advantageous as they provide control and
adherence to specific poetic forms or templates, but they are very
predictable. Heuristic methods, however, are less predictable, and
unpredictability is essential to creative writing. ? (?) proposes a poet-
critic model theory that consists of a computer-based generator and an
evaluator module. The author did not implement the described theory but
proposed a prototype that utilizes the concept of evolution to create original
outputs and uses classical neural networks as evaluators trained on real-world
human evaluations. The evolutionary algorithm is a meta-heuristic approach in
which the best population of poems is chosen by evaluator modules, modified by
crossover operators and mutation operators and continuously evaluated until a
satisfactory result is obtained. ? uses such an approach to enhance poem
features, words and rhymes. ? (?) uses a similar approach with the following
operations: add, delete, or edit as forms of mutation. In a later update of
the framework, ? (?) point to the fact that abusing the poetic license may
lead to non-sense in a poetic form and that poetry generation approaches must
conform to a restricted definition of poetry to generate falsifiable output.
Similarly, ? (?) uses extracted semantic networks, grammar generators, and
syllabic templates to generate Portuguese poems using genetic algorithms. ?
(?) address the lack of story behind the generated poems. The authors avoid to
retrieve from a corpus of existing poems to restrain plagiarism, instead they
choose short similes and non-poetic text corpora. They combine extracted key
phrases from the non-poetic corpus with produced variations of existing
similes to fill a user-given template. They also attempt to automatically
generate a commentary on the generation process by recording context
statements at each step of the generation.

#### Statistical Approaches

A problem in heuristic-based models is that they are not capable of modeling
the language. ? (?) uses statistical methods to analyze a corpus of English
Haikus and generate words related to a theme provided by a user. The authors
pick a real Haiku poem POS template at random and fill-in words from the theme
set. The sentences are then ranked according to the degree of association of
words. In order to augment the unpredictability of the output, they give more
weight to pick second degree association instead of first degree. ? (?) focus
on the use of statistical methods to analyze, generate and translate poems.
They use finite state transducers to map English sentences to a sequence of
syllables and perform unsupervised learning to analyze the stress patterns of
words in a poetry corpus. They used a finite state cascade to generate English
love poems. In addition, the authors used a statistical phrase-based machine
translation to translate Italian poems into English with the help of an iambic
pentameter model. Poetry generation was also formulated as a text
summarization task (?): Given the intentions of a user, write a poem that
meets the poem requirements by retrieving terms from a poetry corpora. The
authors use generative summarization and aim to produce correlated verses
taking into consideration the poetic form and semantic coherence. Word
associations were also used to fill poetry templates (?) replacing words from
a poem with other words based on syntactic similarity. They use the templates
of poems from the Imagist movement to emphasize figurative language.

#### Deep Learning Approaches

Chomsky criticized statistical models as incomprehensible and inefficient
compared to humans (?). The substantial amount of digital data created each
second, the recent advances in computational power, and the ability of deep
learning methods to derive complex data correlations, have led to state-of-
the-art performance in NLG, including poetry generation.

#### • RNN-based

RNNs were first introduced by John Hopfield in the early 1980s. However, they
became popular as language models only a decade ago (?) for their ability to
model long-term dependencies in sequential data such as text. ? (?) use a
Convolutional Sentence Model (CSM) to capture sentence characteristics and use
a character-based Recurrent Neural Network (RNN) model to incrementally
generate poems. A set of keywords provided by the user is expanded with a
poetic phrase taxonomy to generate candidate phrases, the top-ranked phrase
will be the first line in the poem, and the following lines are sequentially
generated given the previous line representations. The training process
considers both the poetic form and semantics. ? (?) designed two RNN-based
models where the first combines char-level and word-level LSTMs with GloVe
word embeddings for the model to capture not only the semantics of the words,
but also morphemes and rhymes during training. The second model uses CNNs to
generate word embeddings. Both models showed significant performance in
coherence, poeticness, and form characteristics over vanilla char-based and
word-based RNN models. ? (?) proposed a sonnet generation system based on RNNs
and finite state acceptors and used embedding rhymes with a word2vec model.
The system is interactive and allows the user to input topic words, adjust
style configurations, and evaluate the generated poem with a 5-star rating;
the evaluation was used to update the system performance. ? (?) proposed a
multi-model sonnet generator trained on Shakespeare’s sonnets by means of
bidirectional word-based and char-based LSTMs with the attention mechanism.
The model consists of language, rhythm and rhyme submodels with Bi-LSTMs with
an attention mechanism; the paper showed that Bi-LSTMs can capture poetic
forms such as rhythm and rhyme very efficiently. ? (?) used phoneme-based and
char-based bi-LSTMs, word-based LSTM, word embeddings and document embeddings
for an author-stylized multilingual text generation framework with an
application on English and Russian poems. ? (?) proposed an encoder-decoder
model with double-layered GRUs using the attention mechanism. The decoder
generated English and French poems line by line starting with the rhyming
words backward to ensure a coherent verse with the forced rhyming scheme. To
show the model’s capability of presenting poeticness from scratch, the authors
trained their model on non-poetic corpora. They proposed a latent semantic
model to ensure topic coherence and a global optimization framework to score
the verses and keep the highest-scoring ones.

#### • RNNs and reinforcement learning (RL)

RNNs are able to handle input sequences of variable length, but they may
suffer from gradient vanishing during backpropagation, which can cause the
loss of distant context in the generated text. Additionally, ? (?) argue that
models based on maximum likelihood estimation, such as RNNs, tend to only
optimize token-level loss rather than considering the poem as a whole, as
humans do. To address this, they applied mutual RL to generate classic Chinese
poems. The authors presented several reward modules instead of maximum
likelihood to better mimic human behavior with reward schemes for fluency,
coherence, meaning, and overall quality. ? (?) used deep RL by combining Bi-
LSTM models and RL in an iterative refinement approach to generate and revise
poems following an author style and a rhyme scheme. ? (?) also proposed a deep
RL using a multi-adversarial CNN-RNN-based GAN model to caption images with a
free verse English poem.

#### • Transformer-based

Transformers (?) were introduced to overcome RNNs limitations caused by their
sequential design, by allowing parallelization of training. Instead of
processing a sequence token by token, sequences are processed holistically
with positional encoding. Additionally, transformers introduced self-
attention, allowing inputs-to-inputs and outputs-to-outputs attention to
enhance context encoding and decoding. ? (?) fine-tuned GPT-2 on a dream
corpus to endow imagery language and fine-tuned it again on various emotion-
based subcorpora to invoke one of the following emotions on the reader: joy,
trust, anticipation, anger, fear, surprise, sadness, and disgust. LimGen (?)
used GPT-2 to predict the next words in multi-candidate templates extracted
from human-written limerick, then filtered out the words for meter and rhyme
consistency, and output the top N lines using a variation of beam search that
calculated a diversity score between templates. ? (?) proposed a structure-
aware training of an autoregressive transformer model to generate formal verse
poems in Spanish and Basque with only non-poetic corpora. Similarly, ? (?)
proposed a multimodel framework trained on non-sonnet corpora to generate
sonnets. The authors separated the training from the decoding and utilize a
series of content planning, rhyme pair generation, polishing and constrained
decoding to generate sonnets.

### Recommendations

A major concern in rule-based and heuristic-based methods is the originality
of language output, particularly when using poem templates and simple synonym
replacements. Similar concerns apply to models trained on poetry corpora and
those using transfer learning to limit mimicking the language, style, theme,
and semantics of the source. Further study on active divergence in NLG can
potentially enhance originality, enabling the production of creative content
that incorporates novel elements. Another important area is optimizing
decoding strategies through the use of enhanced sampling techniques, which can
increase the unpredictability factor of the generated output. Recent
advancements in improving nucleus sampling with randomized heads and
introducing new sampling methods (?) have shown promising results in terms of
diversity and novelty of generated texts. To address the sociability aspect of
poetry, we recommend the utilization of iterative refinement models guided by
discriminator models or reinforcement learning models to provide feedback
during the generation process in a poet-critic-like framework. Additionally,
we recommend developing creative LLMs that integrate elements of poetic
diction during pretraining and incorporating multi-figurative generation
models to aid in effective poetry generation.

## Conclusion

In this work, we bring attention to the problem of generating creative data
for researchers interested in the field. We analyze natural language
generation models based on the three dimensions of creativity we proposed:
originality, sociability and unpredictability. We provide a brief overview of
text generation tasks and their potential creative applications, as well as a
review of recent works. Finally, we provide a comprehensive overview
examination of the task of poetry generation, including the methodologies and
models employed in the literature. We hope that this review encourages
researchers and provides insights for those interested in creativity in
natural language generation and poetry, particularly in considering the use of
suggested creativity and poeticness criteria in language models.

## Acknowledgements

We thank Michael Ullyot for his insightful feedback and the anonymous
reviewers for their valuable input.

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