# A Survey of Text Style Transfer: Applications and Ethical Implications

Sourabrata Mukherjee, Mateusz Lango, Zdeněk Kasner, Ondřej Dušek
Charles University, Faculty of Mathematics and Physics
Institute of Formal and Applied Linguistics
Prague, Czech Republic
<EMAIL_ADDRESS>
###### Abstract

Text style transfer (TST) is an important task in controllable text
generation, which aims to control selected attributes of language use, such as
politeness, formality, or sentiment, without altering the style-independent
content of the text. The field has received considerable research attention in
recent years and has already been covered in several reviews, but the focus
has mostly been on the development of new algorithms and learning from
different types of data (supervised, unsupervised, out-of-domain, etc.) and
not so much on the application side. However, TST-related technologies are
gradually reaching a production- and deployment-ready level, and therefore,
the inclusion of the application perspective in TST research becomes crucial.
Similarly, the often overlooked ethical considerations of TST technology have
become a pressing issue.
This paper presents a comprehensive review of TST applications that have been
researched over the years, using both traditional linguistic approaches and
more recent deep learning methods. We discuss current challenges, future
research directions, and ethical implications of TST applications in text
generation. By providing a holistic overview of the landscape of TST
applications, we hope to stimulate further research and contribute to a better
understanding of the potential as well as ethical considerations associated
with TST.

##  1 Introduction

Text Style Transfer (TST) is a task in controllable natural language
generation that constructs techniques that change the stylistc attributes of a
written text such as politeness, formality, or sentiment, without altering the
rest of the text’s meaning Jin et al. (2022a). Although the general goal of
rewriting a text while preserving its content is relevant to other NLP tasks
such as machine translation Sutskever et al. (2014); Bahdanau et al. (2015) or
summarisation Allahyari et al. (2017), TST is unique in focusing on more
subtle, stylistic aspects of the text. The text style may reflect the writer’s
demographic characteristics, such as gender and age, their educational
background, emotional state, and even situational context, as reflected in the
sentiment and politeness of the text. Figure 1 shows some basic examples of
TST.

Figure 1: Several examples of text style transfer: transferring sentiment,
politeness and formality.

In recent years, TST research has gained considerable momentum. With the
advent of neural networks, text style transfer research has shifted from
traditional linguistic approaches, which relied on hand-crafted grammars and
templates, to data-driven approaches, typically based on sequence-to-sequence
modeling Rao and Tetreault (2018). However, since there is a considerable
dearth of style-specific parallel data, TST research has taken up the
challenge of devising new methodologies, such as latent-space content-style
disentanglement Hu et al. (2017a); Shen et al. (2017), prototype editing Li et
al. (2018), or pseudo-parallel corpora Zhang et al. (2018); Jin et al. (2019)
(see Section 2 for details).

This rapid development of TST technologies has already been covered by several
reviews Jin et al. (2022b); Hu et al. (2022), but they are method-oriented and
do not pay much attention to the research on TST applications, which has also
come a long way. We believe that this application perspective will play an
increasingly important role, as TST-related technologies are gradually
reaching a production- and deployment-ready level, which will boost interest
in application-specific issues. Therefore, in this paper, we provide what we
believe to be the first comprehensive overview of different TST applications
in order to stimulate future research in this area. We analyze TST
applications in user privacy and security, the generation of personalized
texts, and building dialogue agents, as well as the use of TST to improve the
performance of other NLP and machine learning tasks. We also provide a
discussion of the implications of TST applications from an ethical
perspective, draw lines for future research, and review recent LLM-related TST
work that has not been covered in previous reviews.

We begin by defining the task of text style transfer and providing a brief
summary of the models proposed in TST research in Section 2. This is followed
by the main contribution of this paper, which is an overview of different
applications of TST in Section 3. Next, we describe open problems and future
research directions in Sections 4 and 5. Finally, we conclude with a
discussion of the ethical impact of TST applications in Section 6.

##  2 The Task of Text Style Transfer

In this section, we present a basic introductory overview of TST. For more
detail, we refer the reader to the existing general surveys of TST by Jin et
al. (2022b) and Hu et al. (2022).

Text style refers to how individuals express their state of mind, personal
characteristics, and rhetorical choices in their writing. This can be seen in
their word choice, writing style, and other attributes. The style of a text
can also be described using various concepts such as sentiment Mukherjee et
al. (2022), emotion Helbig et al. (2020), humor Hossain et al. (2019), similes
Chakrabarty et al. (2020), personality Kaptein et al. (2015), politeness
Mukherjee et al. (2023b), formality Rao and Tetreault (2018), simplicity Tan
and Goonawardene (2017), or authorship Reddy and Knight (2016a).

TST models aim to change the style of the text while preserving the style-
independent content in the text. Given an arbitrary text, $x$ with a source
style $s_{1}$, our goal is to rephrase $x$ to a new text $\hat{x}$ with a
target style $s_{2}$ ($s_{2}\neq s_{1}$) while preserving its style-
independent content (see Figure 1).

The training process of these models can be broadly classified into four
categories which we discuss in the following subsections: parallel supervised
(Section 2.1), non-parallel supervised (Section 2.2), purely unsupervised
(Section 2.3), and using Large Language Models (LLMs) (Section 2.4). We also
briefly discussed the evaluation process of the TST task in Section 2.5.

###  2.1 Parallel Supervised Approaches

Parallel supervised TST models are trained using similar pairs of texts with
similar content but different styles. The most common underlying architecture
of these models is the sequence-to-sequence architecture, same as for for many
other natural language generation tasks Gatt and Krahmer (2018). Usually, a
sequence-to-sequence model is trained on a parallel corpus wherein the text of
the original style is fed into the encoder and the decoder outputs the
corresponding text according to the target style. For example, Jhamtani et al.
(2017) used this approach to translate between modern and Shakespearean
English (where ample training data exists), while Mukherjee and Dusek (2023)
employed minimal parallel data and various low-resource methods for sentiment
transfer.

Applying the sequence-to-sequence approach to this task is quite challenging
due to the unavailability of parallel data Hu et al. (2022); Mukherjee et al.
(2023a). To handle this, data augmentation methods, such as pseudo-parallel
datasets, have been explored Mukherjee and Dusek (2023); Shang et al. (2019);
Jin et al. (2019); Nikolov and Hahnloser (2018); Liao et al. (2018).

###  2.2 Non-parallel Supervised Approaches

An alternative method to using parallel datasets is the non-parallel
supervised setting. In this setting, the TST models aim to transfer the style
of texts without any knowledge of matching text pairs in different styles.
Data for each individual target style is provided, but not aligned with other
styles.

To train the TST models in the non-parallel supervised setting, researchers
have proposed the strategy of disentangling the style and content in text
Gatys et al. (2015). There are three main types of style-content
disentanglement strategies:

  1. (1)

Explicit Style-Content Disentanglement TST models for explicit style-content
disentanglement adopt a straightforward text replacement approach for
generating texts of a target style. For example, Li et al. (2018) found that
parts of a text that are associated with the original style can be replaced
with new phrases associated with the target style.

  2. (2)

Implicit Style-Content Disentanglement For implicit style-content
disentanglement, TST models first learn the latent representations of the
content and the style of the given text. The latent representation of the
original content is then combined with the latent representation of the
desired target style to generate text in the target style. Techniques such as
back-translation and adversarial learning Shen et al. (2017); Zhao et al.
(2018); Fu et al. (2018); Prabhumoye et al. (2018); Hu et al. (2017a) have
been proposed to disentangle content and style in latent representations.

  3. (3)

No Style-Content Disentanglement The most recent research has also explored
performing TST without disentangling the text’s style and content. These
techniques include attribute-controllable generation, reinforcement learning,
probabilistic modeling Lample et al. (2019a); Dai et al. (2019a); Li et al.
(2019); Luo et al. (2019a); He et al. (2020).

###  2.3 Unsupervised Approaches

Works on purely unsupervised TST are comparatively rarer. An early study by
Radford et al. (2017) explored the properties of recurrent language models at
the byte level. They trained an LSTM model on text processed as a sequence of
UTF-8 encoded bytes in an unsupervised manner. Interestingly, they found a
single neuron within the trained LSTM that directly corresponded to the
sentiment. By manipulating this neuron, they were able to transfer sentiments
in sentences. Xu et al. (2020) used unsupervised representation learning to
separate style and content on a mixed corpus of unspecified styles. They were
able to isolate a latent dimension responsible for sentiment and achieved
satisfactory results in sentiment transfer. Shen et al. (2020) computed a
"sentiment vector" by averaging latent codes separately for 100 positive and
100 negative sentences in the development set, then calculating the difference
between them. Given a test sentence, they changed its sentiment from positive
to negative or vice versa by adding or subtracting the sentiment vector.

The performance of these purely unsupervised TST methods is encouraging.
However, it is important to note that these methods have been evaluated
primarily on sentiment transfer tasks. More research is needed to determine
whether purely unsupervised methods can be generalized to other TST tasks.

###  2.4 Using Large Language Models (LLMs)

Large Language Models (LLMs) have revolutionized the field of natural language
processing by generating coherent and contextually relevant text (e.g.,
Touvron et al., 2023a, b). By learning complex patterns and structures from
vast amounts of text data, LLMs inherently capture various linguistic styles
and nuances. This capability is particularly beneficial for TST tasks.

A distinctive feature of LLMs is their ability to perform valuable tasks
without fine-tuning, showcasing zero- and few-shot capabilities Liu et al.
(2023). Reif et al. (2021) frame style transfer as a sentence rewriting task,
enhancing LLMs’ zero-shot performance for arbitrary TST by using task-related
exemplars. Suzgun et al. (2022) proposed a reranking method to select high-
quality outputs from multiple candidates generated by the LLM, thereby
improving performance. Luo et al. (2023) proposed the use of a prompt-based
style classifier to guide the search for word-level edits of a given text.
Additionally, Liu et al. (2024) introduced dynamic prompt generation to guide
the language model in producing text in the desired style.

While prompt engineering is a prevalent approach Brown et al. (2020); Jiang et
al. (2020), LLMs are highly sensitive to prompts Mishra et al. (2021); Zhu et
al. (2023) and may not always guarantee optimal performance Liu et al. (2024).
Furthermore, Ouyang et al. (2022) indicated that larger LLMs are not always
inherently superior in understanding user intent. Mukherjee et al. (2024b)
demonstrated that despite good results for prompting, finetuning the LLMs
still leads to significant performance improvements.

###  2.5 TST Evaluation

TST tasks are mainly evaluated in three main dimensions: style transfer
accuracy, content preservation, and fluency. Style transfer accuracy is
typically measured using a trained style classifier Jin et al. (2022a).
Previous works Mukherjee and Dusek (2023); Hu et al. (2022); Jin et al.
(2022a) evaluated content preservation using the BLEU score Papineni et al.
(2002a) and embedding similarity Rahutomo et al. (2012) against the input
sentences. Sentence-BERT Reimers and Gurevych (2019) or a similar embedding
representation is used, and then cosine similarity is used for embedding
similarity. The perplexity of pre-trained language models, such as GPT-2
Radford et al. (2019), is used to estimate fluency.

##  3 Applications

We now turn to the core of our paper, i.e. the overview of TST applications.
We grouped the applications into four broad areas: user privacy and security
(Section 3.1), creating personalized texts (Section 3.2), dialog response
generation (Section 3.3), TST used in other NLP tasks (Section 3.4) and TST
for data augmentation (Section 3.5). We provide a high-level overview of TST
applications and examples of these applications in Table 1.

Original Text → Style Transferred Text User Privacy and Security (Section 3.1)
Bias Correction$\colon$ Each of them is physically handicapped. → Each of them
has a physical disability. She was hysterical and overly emotional. → She
expressed her emotions openly and passionately. Fighting Offensive
Language$\colon$ He’s an old geezer who doesn’t understand technology. → He’s
an older individual who may not be familiar with the technology. You’re being
such a retard, it’s not that difficult. → You’re struggling with this, but
it’s not as simple as you thought. Concealing Authorship$\colon$ I suggest we
revise the current strategy to improve our results. → The suggestion is to
revise the current strategy to improve the results. Our research indicates
that this approach is highly effective. → The research indicates that this
approach is highly effective. Creating Personalized Texts (Section 3.2)
Marketing and Advertisement$\colon$ Our company has been in business for 10
years. → With a decade of excellence, our established company brings
unparalleled expertise and experience to cater to your needs. Enjoy the
convenience of online shopping with our user-friendly website. → Experience
seamless and hassle-free online shopping with our intuitive and user-friendly
website! Text Simplification$\colon$ The prevalence of cardiovascular diseases
is higher in individuals with a family history of hypertension and
dyslipidemia. → People with a family history of high blood pressure and high
cholesterol are more likely to have heart problems. The research utilized a
cross-sectional design and collected data through self-report questionnaires
and objective measurements to examine the relationship between sleep duration
and mental health outcomes. → The study used surveys and tests to look at how
sleep duration is related to mental health in a large group of people. Dialog
Response Generation (Section 3.3) Stylized Response Generation$\colon$ .
User$\colon$ Hello there. Chatbot$\colon$ What can I help you with? →
Chatbot$\colon$ Hello! I’m here to assist you with anything you need. How may
I help you today? User$\colon$ I need help with my account. Chatbot$\colon$
What’s the problem? → Chatbot$\colon$ Of course, I would be happy to assist
you with your account. Could you please provide me with some more details
about the issue you are experiencing? Use as Part of Other NLP Tasks (Section
3.4) Stylized Machine Translation$\colon$ Mach endlich die Tür zu! _(Close the
door already!)_ → Could you please close the door? Das geht dich nichts an!
_(That’s none of your business!)_ → I’d rather not share that information.
Stylized image captioning: A man and a boy lying in the snow → Father and son
playing in the snow, enjoying the winter. Front of an old building with a sign
→ A photo of an ugly building with a stupid sign out front.

Table 1: Examples of TST Applications.

###  3.1 User Privacy and Security

TST has several applications in the area of user privacy and security. It can
be used to correct undesirable social biases reflected in text, to protect
users’ mental health by combating offensive language, and to safeguard users’
privacy by disguising their writing style while still conveying the intended
message.

#### Bias Correction

Narratives and news texts sometimes reflect social biases and stereotypes,
like the traditional gender roles Lakoff (1973); Fiske (1993); Fast et al.
(2016). The TST task of controllable text revision constructs methods for
rephrasing a text to a targeted style that can also include reframing its
content. This leads to the development of automatic revision tools that have
already proved useful in reshaping gender roles portrayed in the media Clark
et al. (2018).

Reddy and Knight (2016b) used rule-based lexical substitution to obfuscate
gender in social media and review text. Ma et al. (2020) proposed a
controllable debiasing that aims to rewrite a given text in order to correct
the implicit and potentially undesirable bias in character portrayals. Ma et
al. (2020) proposed a controllable debiasing that aims to rewrite a given text
in order to correct the implicit and potentially undesirable bias in character
portrayals. They masked verbs associated with power or agency in the input
text using a dictionary Sap et al. (2017) and regenerated them using a GPT-
based transformer. The same dictionary is also used during decoding to
increase the probability of more neutral verbs. Pryzant et al. (2020) proposed
an approach to automatically convert subjective text into a neutral point of
view.

#### Fighting Offensive Language

Even though social media platforms have undeniably positive impacts on
facilitating communication, it is often the hub of offensive and abusive
language use which may lead to cyberbullying Beran and Li (2005); Munro
(2011). To solve this issue, many recent studies have focused on developing
machine learning models for detecting hate speech Davidson et al. (2017);
Xiang et al. (2012); Djuric et al. (2015); Waseem and Hovy (2016); Chen et al.
(2012); Founta et al. (2019) and offensive language using various text
classification methods Xiang et al. (2012); Warner and Hirschberg (2012); Kwok
and Wang (2013); Burnap and Williams (2015); Nobata et al. (2016); Davidson et
al. (2017); Founta et al. (2019). However, these techniques only detect the
problem, but do not provide actionable tools to mitigate it Janiszewski et al.
(2021). Such tools could automatically suggest transforming hateful sentences
into non-hateful ones, which is essentially a TST task.

dos Santos et al. (2018) proposed an extension of a basic encoder-decoder
architecture by including a collaborative classifier to deal with abusive
language redaction. They also suggest using TST to prevent online
conversations from derailing by suggesting a more polite alternative to the
offensive message a user is about to post. Logacheva et al. (2022) and Klamra
et al. (2022) collected parallel corpora of toxic and non-toxic texts and used
them to fine-tune transformer-based language models. The multi-lingual aspect
of constructing detoxification tools was tackled by Dementieva et al. (2023)
where a joint supervised learning of translation and detoxification was
considered. An unsupervised style transfer technique for redacting offensive
comments in social media was proposed in Tran et al. (2020). The approach
consists of a retrieve-generate-edit pipeline that edits text in a word-
restricted manner while maintaining a high level of fluency and preserving the
original text content.

#### Concealing Authorship

Text anonymisation is often required when creating public datasets or sharing
data with third parties. It is also useful for countering the problematic task
of author profiling, which aims to extract the identity of the author of the
text (e.g. whistleblowers, dissidents), including privacy-invading
characteristics such as gender and age Schler et al. (2006); Brennan et al.
(2012). TST methods can help protect user privacy by modifying text to
disguise the true identity of users Reddy and Knight (2016a).

Gröndahl and Asokan (2020) proposed a combinatorial paraphrasing method to
hide authorship. The approach applies multiple linguistic rules to paraphrases
produced by a neural method to construct a set of candidate paraphrases, from
which a final text is later selected by maximising the distance to the style
of the original author. Jones et al. (2022) found that pre-trained language
models can successfully transfer the style of a given author to fool
authorship detection techniques. Zhai et al. (2022) proposed adversarial
training that takes into account deobfuscation techniques that attempt to
guess authorship. Fisher et al. (2024) addressed the problem of lack of
supervised data in many domains and proposed constrained decoding, an
inference-time technique applied over small language models to conceal
authorship. The method first extracts keywords from a given text and uses them
as lexical constraints in a constrained diverse beam search to generate a
faithful set of candidate texts. The final output is selected by automatic
metrics of coherence and grammatical correctness.

###  3.2 Creating Personalized Texts

TST has the potential to be used for creating personalized texts, where users
can tailor the writing style of a text to suit their preferences or needs.
This can have applications in various domains, such as content creation,
marketing, and communication, where personalized texts can enhance user
engagement, user experience, and customer satisfaction.

#### Marketing and Advertisement

Multiple studies reveal that the use of convincing text creates an impact on
people’s behavior Chambliss and Garner (1996); Kaptein et al. (2012). This
impact depends not only on the information contained in a text, but also on
the way it is presented Johnstone (1989); Muehlenhaus (2012); Darani (2014).
Insights gained from these studies have been applied to improve the
effectiveness of marketing and advertising strategies and TST techniques can
be used to automatically convert a text into an effective style. Recent
studies have explored this opportunity and developed style transfer strategies
based on a user profile Kaptein et al. (2015). Jin et al. (2020) proposed a
use-case for using these TST methods to make news headlines more attractive.
On the other hand, Li et al. (2021) generated news headlines in a user-
independent attractive style using a special encoder-decoder model combined
with headline prototype retrieval.

#### Text Simplification

TST can be applied to create a bridge between an expert’s language and a
layman’s understanding of it Kennedy (1995); Tan and Goonawardene (2017). The
underlying notion is style transfer which aims to improve the readability of a
text by reducing its complexity, for example by explaining complex terminology
with simple phrases Saggion (2017). A very widely studied use case for these
methods is the simplification of encyclopedia entries to facilitate broader
access to knowledge. Research on this application has included methods for
building parallel corpora Hwang et al. (2015); Kajiwara and Komachi (2016) and
applications of neural techniques Nisioi et al. (2017). More recently, Cao et
al. (2020) have been working on the simplification of medical texts, focusing
not only on simplifying the professional language but also on raising the
expertise level of a layman’s descriptions. To this end, they have collected a
new medical dataset and experimented with two specialized text simplification
and three TST approaches.

#### Writing assistants

TST methods can also be incorporated into automatic writing assistants, which
help to refine human-written texts to make them more suitable for their
purpose Lee et al. (2024). Writing assistants can make a complaint sound more
objective, a written request more polite, or business communication more
friendly or professional, depending on the context Jin et al. (2022b). Writing
assistants also provide the functionality of the text simplification methods
mentioned above, making texts written by experts more suitable for a wider
audience. Another important aspect that has recently been tackled is
preserving the user’s writing style while suggesting corrections Yeh et al.
(2024).

#### Generating similes

Generating similes amounts to finding an effective mapping of properties
between two concepts. It could help in downstream applications such as
creative writing assistance and literary or poetic content creation. While
most of the prior computational work has focused on simile detection Niculae
and Danescu-Niculescu-Mizil (2014); Mpouli (2017); Qadir et al. (2015, 2016);
Zeng et al. (2020); Liu et al. (2018), there are works specifically aimed at
simile generation: Chakrabarty et al. (2020) propose a method for
automatically constructing a parallel corpus by transforming a large number of
similes collected from Reddit to their literal counterpart using structured
commonsense knowledge. Yang et al. (2023) collected a simile dataset for
Chinese by automatically extracting them from a corpus of student
compositions. They also proposed an encoder-decoder architecture with a
retrieval module for vehicle search. He et al. (2023) proposed an automatic
evaluation system for simile generation based on five criteria: relevance,
logical consistency, sentiment consistency, creativity and informativeness.

#### Humor Generation

Whereas understanding humor is already a challenging task that requires both
natural language understanding and common sense knowledge Taylor and Mazlack
(2004); Mihalcea and Strapparava (2005); Purandare and Litman (2006);
Hempelmann (2008), generating humorous texts that match human-created one is
even more difficult He et al. (2019); Luo et al. (2019b). Although humour
generation is often realised as a natural language generation task, e.g. by
completing a given text with a pun He et al. (2019), TST techniques can be
used to convert existing texts into humorous ones. For example, Weller et al.
(2020) used TST to modify existing news headlines and demonstrated in an A/B
experiment that they were just as humorous as those edited by humans.

#### Empathetic Rewriting

As empathetic interactions are strongly correlated with symptom improvement in
mental health care Norcross (2002); Elliott et al. (2018), several studies
have explored the possibility of using TST methods to rewrite human or
computer-generated responses to make them more empathetic. Sharma et al.
(2021) used reinforcement learning to train GPT2-based language models that
performs sentence-level edits to make the response of mental care provider
more empathetic. The resulting method was further refined through better
hyperparameter search and improved training data quality, and used in a
controlled trial where the system was shown to increase conversational empathy
by almost 20% Sharma et al. (2023). A rewriting method that can provide
additional educational feedback to trainee mental health workers has been
proposed by Pérez-Rosas et al. (2023). They constructed a framework that first
uses attention scores to mask style-relevant words in the input sentence, and
then regenerates them with target-style words. The result is a rewriting
template containing a rewritten and original sentence with highlighted
(masked) replaced words, providing interpretable feedback to the user.

###  3.3 Dialog Response Generation

Most prior research on dialogue response generation is based on grammatically
correct and contextually relevant responses Ritter et al. (2011); Chen et al.
(2017). However, these syntactically coherent responses did not always
guarantee an engaging and attractive chatbot. Kim et al. (2019) carried out a
study that showcased the impact of a chatbot’s conversational style on users.
It was noticed that when a chatbot possessed a language style that matched
some basic human personality traits (optimistic, humorous), it scored higher
in user satisfaction and had a higher average number of interaction rounds
Song et al. (2019). These insights can be used to build a more effective
chatbot, which could choose its response style according to the customer’s
persona, profile and the context of the conversation, for example using a more
polite conversational style when dealing with customer complaints.

Nevertheless, stylized response generation faces difficulties in generating
coherent responses with a particular style, especially when the target style
is only embedded in unpaired texts that cannot be used directly to train the
dialogue model Zheng et al. (2021); Silva et al. (2022). This led to the
construction of stylized response generation methods inspired by unsupervised
TST approaches, but also opened up the possibility of using TST methods
directly by applying them to already generated responses to impose on them the
desired stylistic properties.

#### Stylized dialogue response generation

Niu and Bansal (2018) used a supervised style classification dataset to
generate stylised responses without parallel data, but with a trained style
text classifier to train a language model in a weakly supervised manner. Gao
et al. (2019) investigated the generation of stylised responses from non-
stylised dialogue data and non-dialogue text in a given style. They proposed a
special neural architecture consisting of a sequence-to-sequence model for
dialogue data and two autoencoders: one for stylised text and one for dialogue
responses from parallel data. Both autoencoders and the sequence-to-sequence
model share the same decoder and are trained together with multi-task
learning, using specialised regularisers to align their latent spaces. Zheng
et al. (2021) also explored this type of training data and proposed an inverse
dialog model to generate possible preceding dialogue contexts for non-dialogue
data. Such generated stylized pseudo-dialogs are then used to train dialog
model with a joint training procedure. A similar, but simpler approach was
applied by Mukherjee et al. (2023c) who applied a TST model to convert
dialogue responses in the training set to the desired style and later train a
chatbot.

Silva et al. (2022) explored stylized task-oriented dialog agents in a
transfer learning scenario across different domains, and found that direct
stylized response generation consistently produced lower quality results than
the two-step approach involving standard response generation followed by TST.
Rewriting dialog responses was also explored by Sun et al. (2022) in the
context of stylized knowledge-grounded dialogue generation. They proposed
using a neural system to rewrite generated dialog responses to
positive/negative/polite styles by tagging style-related words and replace
them with words associated with the desired style while taking into account
the information from the knowledge graph.

#### Persona-based dialogue response generation

A particular type of style transfer studied in the dialogue community is
related to _persona-based dialogue systems_ , where the generated response
should reflect the style and personal characteristics of a selected dialog
agent’s persona. Li et al. (2016) encoded personas of individuals in
contextualized embeddings that helped in capturing the background information
and style to maintain consistency in the generated responses. The persona
embedding is passed to the model at each generation step. Su et al. (2019)
followed this approach, but used generative adversarial networks and sequence-
to-sequence reconstruction loss to improve both coherence and style transfer.
Zheng et al. (2020) also encoded personas in additional embeddings, but
developed a special attention routing algorithm to dynamically reweight the
influence of persona style and dialogue context during generation. Song et al.
(2020) used persona embeddings to predict the masking of words in the
generated response and then rewrite it to a more personality-consistent one.

Luan et al. (2017) focused on using both dialogue data and non-dialogue texts
written by personas to train the neural response generation model through
multi-task learning. Song et al. (2021) likewise used two types of training
data, but the non-dialogue text corpus was used to improve the consistency of
the generated responses rather than to enable style transfer. Cao et al.
(2022) proposed a model-agnostic data manipulation strategy, including the
transfer of persona features between sentences, to train persona-based
dialogues as standard sequence-to-sequence models. Finally, Xu et al. (2023)
pre-trained a persona augmentation generation model to enable zero-shot
persona transfer.

#### Joint persona-based and stylized dialogue response generation

A special case of joint persona-based and stylized generation was covered by
Firdaus et al. (2022), who focused on generating polite, personalized dialogue
responses in accordance with the user’s profile and consistent with their
conversation history. Based on the user’s profile, they created human
annotated politeness templates and then trained an architecture with two
successive decoding steps: text generation incorporating the persona and
dialogue context, followed by text refinement to incorporate style
(politeness).

###  3.4 Use as Part of Other NLG Tasks

TST integrates with various NLP tasks, such as sentiment analysis, machine
translation, and data-to-text generation. TST can be used to manipulate the
style of the generated text according to specific requirements, e.g. to
translate a formal text to an informal text, or to generate text with a
specific sentiment or emotion. This can improve the versatility and
adaptability of NLP models in generating texts with desired style attributes.

#### Stylized machine translation

It is desirable to have additional control of the style of the translated text
in the machine translation domain. Some of the commonly used styles for TST in
machine translation are politeness Sennrich et al. (2016) and formality Niu et
al. (2017); Wu et al. (2020).

Sennrich et al. (2016) used an additional formality token at the input to the
neural decoder to control the use of honorific terms. Niu et al. (2017)
proposed a re-ranking strategy for phrase-based machine translation that also
takes the expected level of formality as an additional input. Wu et al. (2020)
developed a dataset for training machine translation systems that
simultaneously transfers the style from informal Chinese to formal English. To
mitigate the problem of lack of parallel data for stylised translation for
other styles and language pairs, Wu et al. (2021) proposed using a neural
translation model together with a TST model and training them simultaneously
with bidirectional knowledge transfer. Another method to address this issue
was proposed by Zou et al. (2024), who developed a curriculum pre-training
strategy with four learning tasks of increasing difficulty: standard masked
language modelling (MLM), MLM between sentences in different styles, target
generation based on masked target concatenated with source, and MLM between
source-target translation pairs with masked style tokens.

#### Stylized image captioning

Image captioning is a widely studied problem in the computer vision community,
which aims at factually describing image content. However, it has been found
that generating captions in a less neutral style makes them more attractive
and enables new applications Mathews et al. (2016); Gan et al. (2017). Many
stylised image captioning approaches build complex multimodal architectures,
often inspired by TST research, but some works use TST more directly. Tan et
al. (2022) proposed a framework that first extracts a style representation
from a stylised text-only corpus and then uses it for caption generation. The
style extraction is performed by adopting a TST method for disentangled
representation John et al. (2019). Wu et al. (2022) proposed a multi-pass
decoding process using the TST module as the final processing stage.

#### Stylistic summarization

Another natural language generation task that is sometimes considered in
conjunction with TST is text summarisation. Chawla et al. (2019) used a style
classifier-based score to define a reward function for reinforcement learning
of a stylised summariser. Cao and Wang (2021) developed two inference-time
strategies to control the style of generated summaries. One restricts
vocabulary with word unit prediction and the other uses a style classifier to
guide text generation. Li et al. (2021) proposed an architecture that
disentangles style and content from selected prototypes and then uses the
style representation during summary generation. Goyal et al. (2022) showed
that a model with a mixture-of-experts decoder can automatically learn
different summary styles under standard cross-entropy loss.

#### Stylized data-to-text generation

Data-to-text is a natural language generation task that involves generating
text describing facts from structural data (e.g. RDF triples, tables) Lin et
al. (2024). Jing et al. (2023) investigated stylised data-to-text generation
of product specifications on e-commerce platforms, as they noticed that
advertising texts on different platforms use different text styles (more or
less formal). They experimented with different combinations of standard data-
to-text models with two TST approaches: StyleT Dai et al. (2019b) and NAST
Huang et al. (2021), as well as an end-to-end solution.

#### Software engineering

Just like the text in natural language, the program’s code has a style that
encompasses code formatting, naming convention and the use of particular
programming language features to express a given functionality. Some simple
code stylistic features can be applied via hard-coded rules (e.g.
indentation), but others require more advanced solutions. This is why the
software engineering community started exploring TST methods. Munson et al.
(2022) fine-tuned a neural language model for code style transfer in Python.
This was followed by Ting et al. (2023) who constructed a tool using such
trained models. Chen and Abedjan (2023) improved code style transfer
performance by combining code generation with code retrieval from a large code
database.

###  3.5 TST for data augmentation

TST methods have also been applied to tasks not directly related to generating
natural language in a particular style. They can be used to generate
artificial training data to obtain better generalizability, obtain special
examples to attack neural language models, and even to generate counterfactual
examples to explain model predictions.

#### Robustness to variability

Since TST methods aim to rephrase a text without altering the text content,
they can be used to augment data for most non-style-related NLP tasks.
Although using style transfer methods for this purpose is popular in the
computer vision community Zheng et al. (2019), the number of approaches
involving text is surprisingly low. Hu et al. (2017b) used TST to generate
training data for sentiment classification. Chen et al. (2022) approached low-
resouce named entity recognition by using TST together with a special
constrained decoding algorithm to produce only training examples with correct
tagging.

Wei et al. (2023) proposed using TST with back-translation augmentation to
improve general, non-stylized machine translation systems. It has been found
that most of the parallel corpora used to train such systems are produced by
human translators who have a different style to the naturally occurring input
van der Werff et al. (2022). Since this limits the performance gains from
back-translation, Wei et al. (2023) proposed using TST with back-translation
to transfer the style of the training texts to the naturally occurring style.
Experimental comparison with several variants of back-translation showed
improved performance on several popular MT metrics.

#### Robustness to attacks

Several techniques for attacking deep NLP models use TST techniques. Qi et al.
(2021) observed that stylistic features are irrelevant to many NLP tasks and
can thus be used to construct efficient adversarial and backdoor attack
methods. Pan et al. (2022) showed a backdoor attack based on generating texts
with certain stylistic features via TST and demonstrated its effectiveness in
bypassing existing defences. Similarly, Li et al. (2024) proposed the use of a
multistyle backdoor attack. Kang et al. (2024) used TST to expand the search
space of an optimisation algorithm that searches for the most effective
adversarial example. Note that just as TST techniques can be used to attack
models, they can also be used to defend them. For example, adding adversarial
examples to training sets has been shown to improve model robustness Chen and
Ji (2022).

#### Explainable AI

Generating counterfactual explanations is a popular way of explaining
predictions of black-box machine learning models Stepka et al. (2024). A
counterfactual example is an instance that is similar to the one for which the
prediction was made, but the model’s decision was different. Among the many
possible methods for counterfactual generation, Wang et al. (2021) suggested
using TST methods for this purpose in the medical domain. They adapted the
delete-retrieve-generate TST approach Li et al. (2018) to delete sets of
undesirable attributes from electronic health records, retrieve them from
similar healthy patient data, and generate the final counterfactual. Their
approach has been further extended to take into account concurrent treatments
and the patient’s medical history Wang et al. (2023).

##  4 Open Problems

Many aspects of TST still require deeper understanding, including how to learn
good style representation, whether some styles are easier to represent than
others, how to measure content preservation, and what is modeled in
disentangled style-content text representations. The lack of understanding of
these issues correlates with the applicability of TST in real-world scenarios.
We have organized our discussion of open issues in TST around three main
themes: evaluation, multiple style transfer and multilingualism - each of
which is described in a separate section below.

#### Evaluation Metrics

The existing evaluation metrics for TST are relatively limited Pang (2019);
Pang and Gimpel (2018); Mir et al. (2019); Yerukola et al. (2023).

A commonly used metric for transfer accuracy (how well the target style was
matched) is based on a style classifier Jin et al. (2022b). However, the style
classification task does not involve measuring the strength and
appropriateness of the style in a given text, not to mention that the
classifier’s accuracy is not perfect. It should also be noted that training a
style classifier requires supervised data,111Using an unlabelled corpus of
stylised texts combined with a general collection of texts for negative
examples might be sufficient to train a style classifier, but the
classification accuracy of such an approach may vary. which is available for a
very limited number of domains. Although unsupervised TST methods have been
proposed Radford et al. (2017); Xu et al. (2020); Shen et al. (2020), they
still need labeled data for evaluation, limiting their impact in real-world
applications. Current transfer accuracy metrics also do not take into account
the context of the rewritten sentence, which has a significant impact on how
humans evaluate them Yerukola et al. (2023).

Moreover, a suitable metric for measuring content preservation has not been
established yet. Most commonly, BLEU Papineni et al. (2002b) is computed
between transferred sentences and their source, as the n-gram overlap with the
source is considered a proxy for content preservation. Cosine similarity
Rahutomo et al. (2012) between the embeddings of the original and the
transferred sentence is also sometimes used as a metric Fu et al. (2018). This
methodology follows the idea that the embeddings of the two sentences should
be close to each other if most of the semantics is preserved. However, none of
the automatic metrics are able to evaluate style transfer methods specifically
in terms of content preservation in style transfer Toshevska and Gievska
(2021).

The main reason behind these limitations is the entanglement between the
semantic and stylistic properties of natural language. It is very difficult to
separate these two properties because changing one often affects the other.
Thus, balancing the trade-off between style transfer accuracy and content
preservation poses a challenge for the objective evaluation of TST tasks.
Further analysis is also needed on how well models can represent different
styles and how this affects the performance of their transfer. Better
evaluation methods and tools for analyzing the weaknesses of TST models would
provide a better way of assessing their usefulness in different real-world
applications.

#### Multiple TST

Up until now, most works explored in TST are related to binary style transfer.
However, in many real-world applications, performing multiple style transfers
at the same time is required. For instance, transferring both the formality
and the demographic style could make the text more appealing to a certain
audience. Multiple TST is very challenging and very little explored. Lample et
al. (2019a) proposed techniques to do such multiple attributes style transfer
together. The specification of multiple attributes makes the TST task more
complex and realistic.

#### Multilinguality

Most previous works on TST have been done in English, neglecting other
languages for which language-specific TST applications could be developed. For
example, an interesting application of TST could be localizing a text (e.g.
using locally known named entities) after translating it into another
language. Mizukami et al. (2015) incorporated individual Japanese writing
styles in a work focused on statistical machine translation. Garcia et al.
(2021) and Krishna et al. (2022) aim to address the lack of non-English works
in a principled way by studying multilingual TST in low-resource scenarios.
Garcia et al. (2021) leverage a large pre-trained multilingual model and fine-
tune it to perform general-purpose multilingual attribute transfer. Krishna et
al. (2022) proposed an approach for few-shot style transfer in low-resource
multilingual settings. They introduced DIFFUR, a model that enhances few-shot
style transfer by modeling stylistic differences between paraphrases,
achieving significant improvements in formality transfer across seven
languages. Furthermore, Mukherjee et al. (2024a) focus on sentiment transfer
in eight Indian languages, introducing dedicated datasets and evaluating
various benchmark models, including both low-resource parallel and non-
parallel approaches. Further down the road, this line of research could lead
to the development of multilingual TST applications.

##  5 Future Directions

Other potential future directions for TST research include:

  * •

Document-level TST Existing TST techniques are mostly operating on the
sentence level. Document-level TST can eventually help to transfer the style
of full books or Wikipedia articles or to simplify medical and law documents,
keeping the transfer consistent across the document, similar to today’s
document-level machine translation Bao et al. (2021). An interesting approach
to document-level TST could be incorporating contextual TST Cheng et al.
(2020).

  * •

Dynamically-tailored content TST built upon smaller models with lower latency
Fournier et al. (2021) could be used for generating dynamically-tailored
content in real-time, e.g., for targeting automatically generated reports or
news articles to a particular audience.

  * •

Domain independence More domain-independent TST approaches could enable using
a single model for multiple applications. For example, words such as “
_delicious_ ” and “ _tasty_ ”, convey positive sentiment in restaurant reviews
but not in movie reviews where the words “ _imaginative_ ”, “ _hilarious_ ”,
or “ _dramatic_ ” are more indicative. Domain shift Gretton et al. (2009) thus
results in feature misalignment. Only little has been explored in Li et al.
(2019) regarding domain adaptation TST. Their approach includes the use of
domain discriminators to align features from different domains, which helps in
adapting the domain shift issue and enhances the generalizability of TST
models across various domains.

  * •

Combining existing TST approaches Some of the existing TST methods could be
combined or extended to find their way into new application areas without the
need to collect new data sets and build new architectures. For example, a text
paraphrasing system could be built as an ensemble of multiple TST systems
(e.g. for formality, politeness, simplification) with the final result
selected by a reranking mechanism. Another possibility is to combine TSTs such
as politeness, formality and humor adaptation to localise content for
different cultures and to adapt marketing texts to the cultural nuances of
different target markets.

  * •

Improved style representation Constructing a latent representation of text
which preserves its meaning while abstracting away from its stylistic
properties is not trivial Lample et al. (2019b), and even impossible in theory
without inductive biases or other forms of supervision Locatello et al.
(2019). New ways of representing text independently of its style could have a
long-lasting impact on TST research.

There are even more potential TST application areas, which open new directions
for future work. For instance, TST can be used for data augmentation by
generating the same text with different emotions, which may be used for tasks
such as automated story plot generation. A similar approach has been already
explored in image style transfer Zheng et al. (2019); Jackson et al. (2019).
TST can also be used in today’s marketing-dependent and social network-based
scenarios, e.g., for generating more attractive headlines. Furthermore, TST
can be used for changing time- and socio-dependent elements of a text, which
may help to attract new generations of readers to historical texts. The nature
of paraphrasing generation Madnani and Dorr (2010) shares a lot in common with
TST. Many trained TST models can be borrowed for paraphrasing, such as
formality transfer and simplification. By paraphrasing the texts and
conversations intended for adults, TST can be also used for making the content
more amenable to children.

Also, sentiment transfer applications remain largely unexplored despite having
immense potential in various fields such as customer service, social media,
marketing, and news coverage. For instance, they can help provide more
balanced and neutral responses to customer complaints, balance the tone and
sentiment of social media posts, create engaging marketing content while
preserving the core message, and provide a balanced and neutral view of events
and topics in news coverage. In summary, sentiment transfer has the potential
to create more positive and engaging content while providing a balanced and
neutral perspective on information.

##  6 Ethical Impacts of TST Applications

TST has to deal with the same ethical challenges as natural language
processing in general, which are mostly in understanding and mitigating the
bias in data and algorithms Prabhumoye et al. (2021). This is an especially
urgent problem for TST models based on language models trained on massive
uncensored datasets, as task-specifically finetuned models inherit risks of
their base models Weidinger et al. (2022). TST also has to follow general
principles while working on topics that involve human subjects or have direct
applications to humans Beauchamp and Childress (2001a). More specifically, the
work on TST involves two particular ethical considerations: social impact and
data privacy.

TST can be used in negative propaganda Bernays (2005); Carey (1997), for
instance, it can be misused by fraudulent sellers to manipulate the polarity
of reviews to their benefit, utilizing sentiment transfer Prabhumoye et al.
(2018), and for generating malicious texts which can be harmful to people in
various scenarios. There have been reports of several victims who tended to be
suicidal due to the stress from cyberbullying Kowalski et al. (2014). The
stress may further lead to detachment from family Oksanen et al. (2014). This
effect is particularly amplified amongst young social media users.

TST can also be used to make fake text highly persuasive and tailored to
specific user groups by mimicking their communication style. The given text
can even be adapted to the preferences of a particular user, for example when
the system is used as a social bot. An example cited in Jin et al. (2022b)
showcases how social bots played a significant role in the 2016 United States
presidential election Bessi and Ferrara (2016); Shao et al. (2018) by being
responsible for 19% of the total tweets. It is a prominent example of how TST
may be used to manipulate major political decisions. As suggested by Hu et al.
(2022), social bots could also be used as an army with different persuasive
styles to promote anti-social behavior such as online hate speech or
cyberbullying against individuals or groups.

Another potential major negative consequence of TST applications is data
privacy. TST requires collecting huge amounts of data like ratings of reviews,
which may also include sensitive information such as the gender Prabhumoye et
al. (2018) and age Lample et al. (2019a) of the user. To avoid such negative
scenarios, Leidner and Plachouras (2017a) proposed a framework that encourages
organizations to build natural language processing applications with ethical
considerations. This framework could also be applied to TST research wherein
researchers could include an ethical review process to examine the research
and development of TST applications.

Regarding social impact, an important application of TST is changing malicious
and abusive texts into positive ones (see Section 3.1). TST can help to remove
hate speech, offensive language, sexist and racist language, aggression,
profanity, cyberbullying, harassment, trolling, and toxic language Waseem et
al. (2017). A neutral tone is something that the majority of the audience
expects and demands. In the same context, a tool that can automatically detect
subjectively-toned language and suggest neutrally-toned counterparts is very
useful Pryzant et al. (2020). The same tool can also be helpful for providing
reading assistance to alert readers when subjectivity bias is hidden within
any context Pryzant et al. (2020). This tool can also help readers by ensuring
that the text is presented in a neutral and unbiased manner Pryzant et al.
(2020), which is especially important to maintain objectivity in news,
encyclopedias, and other informative content. The automatic offensive-to-
neutral language transfer can be used for social chatbots to ensure the
generated content does not contain any offensive language Roller et al. (2020)
and to purify malicious content on social media.

The goal of a discussion on ethics is to take into account various concerns
like how a system should be built, who it is intended for, and how to assess
its societal impact Hovy and Spruit (2016); Beauchamp and Childress (2001b).
Instead of abandoning the whole idea of building such tools, one must explore
the concerns and find ways to deal with them Leidner and Plachouras (2017b).
This should be viewed as an opportunity to increase transparency by surfacing
the risks and finding the best ways to its strategy into practice.

##  7 Conclusion

In this survey, we provided a comprehensive overview of the applications of
TST. We also discussed the problems and ethical impact of TST and provided
directions for future work in the domains that can make use of future TST
developments. We hope that this contribution will not only stimulate TST
research by highlighting potential application perspectives and problems
encountered in specific applications, but will also be useful to researchers
and practitioners in other fields who are considering the use of TST in their
work.

## Acknowledgements

This research was funded by the European Union (ERC, NG-NLG, 101039303) and by
Charles University projects GAUK 392221 and SVV 260698.

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