# A Review of Text Style Transfer using Deep Learning

Martina Toshevska and Sonja Gievska
Faculty of Computer Science and Engineering  Ss. Cyril and Methodius
University - Skopje
North Macedonia
Email: {martina.toshevska<EMAIL_ADDRESS>
###### Abstract

Style is an integral component of a sentence indicated by the choice of words
a person makes. Different people have different ways of expressing themselves,
however, they adjust their speaking and writing style to a social context, an
audience, an interlocutor or the formality of an occasion. Text style transfer
is defined as a task of adapting and/or changing the stylistic manner in which
a sentence is written, while preserving the meaning of the original sentence.

A systematic review of text style transfer methodologies using deep learning
is presented in this paper. We point out the technological advances in deep
neural networks that have been the driving force behind current successes in
the fields of natural language understanding and generation. The review is
structured around two key stages in the text style transfer process, namely,
representation learning and sentence generation in a new style. The discussion
highlights the commonalities and differences between proposed solutions as
well as challenges and opportunities that are expected to direct and foster
further research in the field.

Motivated by recent advancements in the field, we have carried out a
systematic review of state-of-the-art research to highlight the trends,
commonalities and differences across style transfer methodologies using deep
learning. The discussion is organized around key stages of the process,
namely, representation learning of style and content of a given sentence, and
generation of the sentence in a new style. A comprehensive view of
methodologies, available datasets and evaluation metrics is compiled to foster
further research in the field.

Text Style Transfer, Deep Learning, Natural Language Processing, Natural
Language Generation, Neural Networks

##  1 Introduction

Naturally occurring linguistic variations in spoken and written language have
been contributed to culture, personal attributes and social context [1, 2].
The underlying factors contributing to linguistic variations in spoken
language have been extensively studied in the field of variationist
sociolinguistics. The adjustments of one’s individual style to match or shift
away [3] from the style of the interlocutor, the audience or social context
are prominent in the work of the American linguist, William Labov [4, 5, 6].
Different people have different ways of expressing themselves [4] and personal
attributes, such as gender, age, education, personality, emotional state [7]
are reflected in their writing style. However, style changes over time [1] and
we adjust to a social context, an audience we address, a person we communicate
with [8], and/or the formality of an occasion [3]. While direct mapping of
sociolinguistics categories is not always possible, stylistic properties have
been classified along several dimensions in the research on natural language
understanding and generation.

Adjusting the style of a sentence by rewriting the original sentence in a new
style, while preserving its semantic content, is referred to as text style
transfer. The diversity of linguistic styles is matched by the diversity in
research interests in the field. Some researchers viewed style transfer as an
ability to adjust the emotional content in a written text; others equated the
concept with formality or politeness. Changing the sentiment polarity of a
sentence might change the meaning of a text or transform the message it
conveys, although the ability to change the emotional content in a written
text should be viewed more along the lines of adjusting the tone of a message
that is more appropriate, emphatic and less severe or offensive to the
audience or the conversational partner. Other researchers have directed their
efforts towards much more sound conceptualization of a style as a genre, or
linguistic style of a person, or a particular social group.

Language style should be a special consideration in current and future
intelligent interaction systems [9] that understand, process, or generate
speech or text. Automatically adjusting the text style could help users
improve their communication skills (e.g., being more polite, learning to write
formal messages), and could become even more important, when employed in
future prosocial interaction mediators on discussion platforms and comment-
based communities (e.g., toning down negative sentiment, neutralizing
offensiveness).

In a decade or so, the work on the topic expanded from a few articles to an
active research area. Most of the methods for text style transfer are based on
deep neural networks. The success of deep learning in other areas has provided
fruitful directions to be followed. Inspired by the success of the encoder-
decoder models in other fields, including machine translation (MT) [10, 11],
text summarization [12] and dialogue generation [13], a number of style
transfer models are built upon this end-to-end model of learning [14, 15, 16,
17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31]. New advances
directed toward adversarial learning have also inspired more recent works on
text style transfer [32, 33, 34, 35].

Pivotal in this review are the studies that address the automatic adjustment
of the style of a written text. At the onset of our paper, we introduce the
reader to various text styles that have been in the focus of the selected
research papers. We have compiled a list of publicly available datasets that
we discuss in terms of their suitability for a particular style transfer
task(s). The evaluation of how successful a particular model is on the task of
style transfer has two objectives: to measure how well the semantic content in
the generated sentence was preserved and to assess the quality of rewriting
the sentence in a new (target) style. Evaluation of the performance of style
transfer models is of special importance for future research in the area.

The discussion of the specifics of the proposed approaches to style transfer
is organized to allow readers to follow the advances in deep learning and
their impact on style transfer tasks. The discussion follows the two key
stages in the text style transfer process: 1) representation learning of the
style and content of a given sentence and 2) generation of the sentence that
has the same meaning as the input sentence, but is expressed in a different
style. Auxiliary elements, such as style embeddings [14, 15, 17, 18, 19, 20,
23, 29, 31, 32, 34, 35], style classifiers [16, 17] and/or adversarial
discriminators [32, 33] are also discussed. We discuss the critical stage of
the process, the output sentence generation by categorizing the approaches
among three groups. Namely, models that use a simple approach to generation by
reconstructing the input sentence, models that incorporate additional style
classifier in their encoder-decoder architectures, and models that adopt
adversarial learning.

The paper is organized as follows. After the introductory section, Section 2
provides a description of various text styles that have been in the focus of
the selected research papers. Section 3 gives a formalization of text style
transfer and discusses the publicly available datasets suitable for the task
at hand. The discussion of a set of measures, which have been proposed as
meaningful criteria for evaluating style transfer models, is also presented.
Beginning with a brief introduction of several deep neural networks in Section
4, the discussion of state-of-the-art style transfer methodologies using deep
learning is presented in Section 5. Section 6 reflects on the challenges style
transfer faces and casts light on potential research directions that are
expected to further advance the field. Section 7 concludes the paper.

##  2 Text Style

The nuance and subtilty of language variations are functions of individual,
social as well as situational differences. Emerging research on automatic
style transfer of written text converges toward a common view that style is an
integral part of a sentence, indicated by the choices of words a person makes
[36]. We provide an introduction of various text styles that have been in the
focus of the research on the automatic style transfer. In particular, a short
description of the following linguistic styles is given: individual style,
genre, as well as the formality, politeness, offensiveness, and sentiment that
is conveyed by a given text.

###  2.1 Personal Style

The words people use reveal a lot about themselves, such as their personality,
gender, or age [7]. There are several studies examining language variations
across different gender and age groups found in formal texts [37], social
media [38, 39], and blog posts [40, 41]. The findings suggest that differences
in language usage between various demographic groups do exist and the
identification of the author’s gender and/or age could be done with an
accuracy of 80% on the basis of usage of specific words [42]. For instance,
female users tend to use more emoticons [43] and choose words with positive
emotional connotation [44]. On the other hand, the study of online language
highlights the differences between various age groups - younger people use
chat-specific e-language and refer to themselves more frequently, while older
people use more complex sentences and include more links and hashtags [45].

These findings could be fruitfully applied in human-computer interaction [9]
as important interaction features that develop user trust and satisfaction. A
key challenge in designing believable virtual assistants is endowing them with
dialog capabilities that are not only responsive to the user’s need, but have
a style of their own that matches the user’s language style.

Shakespearean writing
style111https://en.wikipedia.org/wiki/Shakespeare’s_writing_style, last
visited: 09.03.2021 has been recognized as a specific writing style. An
interesting research task of rewriting sentences in Shakespearean style have
been reported in [46] that could be potentially used for edutainment purposes.
Research on generating image caption used a rather unorthodox approach to
generating caption in a style that was learned from romance novels and Taylor
Swift’s song lyrics222https://medium.com/@samim/generating-stories-about-
images-d163ba41e4ed, last visited: 01.04.2021 [47, 48].

###  2.2 Formality

Language style is very often associated with register i.e., formality of a
given text. There is no unified definition of what formal language is and yet,
the distinction between the language used in formal and informal settings is
well recognized. For example, the language in academic papers is considered
more formal than the language used in social media. Longer texts as well as
texts containing passive voice tend to be perceived as more formal [49]. The
formal style of writing is usually characterized by detachment, precision,
objectivity, rigidity, and higher cognitive load [50]. On the other side,
texts that contain short words, contractions, and abbreviations are considered
informal [49]. The informal style is more subjective, less accurate, less
informative, and with a much lighter form [50]. Indicators of formality
considered in the research on automatic formality detection include the use of
slang and grammatically incorrect words [51], social distance, and shared
knowledge between the writer and the audience [52]. Automatically improving
the level of formality of a written text is a useful feature incorporated in
writing assistants [53].

###  2.3 Politeness

The politeness of the language we use is affected by the social distance
between the writer and the audience [54, 55, 56]. The level of politeness is
important for “maintaining a positive face” in social interaction with others
[57] and it plays a significant role in the overall experience of
communication [58]. Polite and impolite are located on the opposite sides of
the spectrum, although different levels of politeness might be used. A study
presented in [59], shows that high frustration is correlated with a writing
style that is less polite and less formal. Systems for automatic adjustment of
politeness could safeguard online writing, especially in a situation when
someone (unintentionally) writes an impolite text that will be received and
read by others.

###  2.4 Offensiveness

The damaging consequences of malicious online behavior in the form of hate
speech, trolling, and use of offensive language remain a recurrent problem for
almost any social media platform. Devising systems and establishing
interaction mediators that will automatically identify, remove, and/or label
posts with offensive language and hate speech is demanded by public,
governments, and institutions.

Detecting offensive language is a widespread research area that focuses on
determining whether a sentence is offensive or not [60], or determining the
audience that is targeted by a message (group or individual) [61]. Studies
show that usage of specific words might correlate with offensive language. For
example, words, such as ”killed”, ”fool”, ”ignorant” are often correlated with
offensive language [62]. The potential benefit of a style transfer system to
neutralize offensive remarks before they are posted is welcomed by many social
media and comment-based news communities.

###  2.5 Genre

Genre of a document is determined on the basis of some external criteria [63],
such as purpose and target audience [64]. News, advertisements, and technical
reports are some of the genres text documents are categorized into.
Identifying the genre of a document could potentially improve Information
Retrieval systems by search results that match or are relevant to a particular
user’s search. For example, when one intends to buy something, advertisements
might be more relevant than scientific reports [65].

A document written in a style that matches the language style used by a
particular group of people is expected to be more understandable by the target
audience. For instance, medical reports are often difficult to understand by
non-experts in the field. Automatically transforming a medical report into a
document in layman terms, might improve its readability by a wider audience.

###  2.6 Sentiment

Emotions play a crucial role in human behavior [66] and one’s emotional state
is often reflected in one’s spoken or written language. While emotional
connotation carried by a sentence may not be a typical stylistic variation of
language, rewriting a sentence with toned down negative emotions might be
desired in many applications. Several categorical and dimensional models for
emotions have been proposed [67, 68, 69, 70]. Detecting sentiment polarity of
a text i.e. whether the overall sentiment of a particular text is positive or
negative [71, 72, 73, 74, 75, 76] have been used in predictive analytics.
Being able to detect emotions expressed in online posts have been used to
“sense the mood of a community” [77, 78, 79, 80], opinion of the public about
specific events [81], the emotions embedded in news headlines [82], or
political sentiment [83]. Sentiment polarity has been used for predicting the
impact of users’ reviews on book sales [84], sales performance prediction
[85], ranking products based on user reviews [86], stock market prediction
according to Twitter moods [87], website popularity prediction [88], etc.

##  3 Style Transfer for Text

###  3.1 Style Transfer Tasks

Text style transfer refers to the process of rewriting a sentence in a new
style, which involves generating a new (output) sentence that has the same
explicit meaning as the original (input sentence), while stylistically
differing from the original one. Style transfer has been applied to adjust,
modify or adapt the manner in which a sentence is written. The term style has
been used rather broadly and encompasses properties, such as: register
(formality), politeness, offensiveness, genre according to purpose, genre
according to the target audience, sentiment or the individual style of the
author or the social group they belong to. Table 1 presents illustrative
examples for each of the style transfer tasks that have been given attention
in research literature.

The objective of each style transfer task is to adjust the style of a sentence
with respect to particular style properties. For example, adjusting the
emotions conveyed in a sentence is referred to as sentiment style transfer.
Adjusting the politeness or the formality of a sentence is associated with
politeness and formality transfer, respectively. Removing the offensiveness
and substituting it with a neutral style has been the objective in the task of
transferring offensive to non-offensive text. Rewriting a text that
stylistically adheres to the personal writing style of an author (e.g.,
Shakespeare writing style, Taylor Swift’s lyrics) or a social group (e.g.,
masculine vs feminine language style, democrat vs republican language) is
referred to as personal style transfer. Genre style transfer could be related
to a purpose (i.e. advertisement or news articles are written in a different
style) or the intended audience (e.g., content written in expert language vs
layman language).

Task | Input sentence (style 1) | Output sentence (style 2)
---|---|---
Sentiment style transfer |  Great food, but horrible staff and very very rude workers! (negative) |  Great food, awesome staff, very personable and very efficient atmosphere! (positive)
Politeness transfer |  Send me the data. (non-polite) |  Could you please send me the data? (polite)
Formality transfer |  Gotta see both sides of the story. (informal) |  You have to consider both sides of the story. (formal)
Transferring offensive to non-offensive text |  I hope they pay out the ***, fraudulent or no. (offensive) |  I hope they pay out the state, fraudulent or no. (non-offensive)
Personal style transfer (Shakespearean) |  My lord, the queen would speak with you, and presently. (shakespearean english) |  My lord, the queen wants to speak with you right away. (contemporary english)
Genre based on audience (expert/layman) |  Many cause dyspnea, pleuritic chest pain, or both. (expert) |  The most common symptoms, regardless of the type of fluid in the pleural space or its cause, are shortness of breath and chest pain. (layman)
Table 1:  Illustrative examples of selected style transfer tasks.

###  3.2 Datasets

A number of datasets have been used in the research on style transfer of text.
The list of publicly available datasets targeted by the research on style
transfer offered in this paper is presented in Table 2. Each dataset has been
described with the following attributes: the year when a dataset has been
published, whether the dataset is composed of parallel text sample pairs or
not, type of text (e.g., emails, reviews, tweets, posts, documents), the
number of text data samples, labels for the style used, as well as references
to studies that have previously used the dataset.

A short description of the datasets, divided into parallel and non-parallel is
presented below. The number of parallel datasets suitable for style transfer
is limited since creating a large number of pairs of text (e.g., sentences,
paragraphs, documents) containing sentences that express the same meaning in a
different manner requires a lot of human work. In non-parallel datasets there
are no paired data to learn from. The number of these datasets is larger
because most of them are subsets or adapted versions of datasets that have
been previously created for other tasks, such as sentiment analysis, author
profiling, genre classification, etc.

####  3.2.1 Parallel Datasets

Shakespeare333https://github.com/cocoxu/Shakespeare, last visited: 28.08.2020
dataset [89, 90] contains 21,075 sentence pairs from 16 Shakespeare’s plays
and their line-by-line paraphrases in contemporary English. A style transfer
task on this dataset has been defined as a transformation of a sentence
written in contemporary English into a sentence written in Shakespeare’s
language style.

GYAFC444https://github.com/raosudha89/GYAFC-corpus, last visited: 28.08.2020
dataset (Grammarly’s Yahoo Answers Formality Corpus) [53] is a parallel
dataset of formal and informal sentence pairs. A subset of informal sentences
is selected from the Yahoo Answers L6
corpus555https://webscope.sandbox.yahoo.com/catalog.php?datatype=l, last
visited: 28.08.2020. For each sentence, a formal version is written by people
recruited through Amazon Mechanical Turk (AMT). The final dataset contains
112,975 pairs of informal-formal sentences and 111,266 pairs of formal-
informal sentences.

Cheng et al. [18] have created a dataset containing informal and formal
versions of 600,000 email messages from the Enron corpus [91]. The AMT
annotators were asked to identify informal sentences in each email and rewrite
them in a formal style.

Captions [92] is composed of 7,000 image captions that were classified as
factual, romantic, or humorous. For each image, a caption in all three styles
is created, making the dataset appropriate for a stylistic transformation of a
sentence among the three alternate styles.

####  3.2.2 Non-parallel Datasets

Yelp666https://www.yelp.com/dataset, last visited: 13.03.2020 dataset is a
collection of 8.6 million business reviews that are classified as positive or
negative according to their 5-star rating system, making the dataset suitable
for sentiment style transfer. The dataset was often used to train systems for
changing the polarity of a given text.

Gender [93] is a subset of Yelp reviews annotated with gender labels (male and
female) that were assigned by inferring the gender of the review’s author by
his or her first name. This subset is suitable for rewriting text written by a
female in a masculine writing style (and vice versa).

Amazon777http://jmcauley.ucsd.edu/data/amazon/, last visited: 13.03.2020
dataset [94] of 1 million product reviews,
SST888https://nlp.stanford.edu/sentiment/treebank.html, last visited:
27.08.2020 (Stanford Sentiment Treebank) [95] consisting of 9,613 movie
reviews, and IMDB [96] dataset composed of 350,000 movie reviews have been
labeled with sentiment polarity making them often used for sentiment transfer.

Paper-News Titles dataset [19] contains 200,000 titles categorized into two
groups: titles of scientific articles and headlines of news articles. The news
are collected from the UC Irvine Machine Learning Repository and the papers
were compiled from publishing websites, such as ACM Digital Library, Springer,
Nature, Science Direct, arXiv, and others.

Gigaword dataset [97, 98] is composed of 4 million news articles from seven
news media publishers. The headlines of the news articles have been labeled
according to their publisher.

Political slant [99] is a dataset composed of 540,000 comments on Facebook
posts from 412 members of the United States Senate and House of
Representatives. Every comment is categorized as either democratic or
republican on the basis of the political affiliation of a member.

dos Santos et al. [20] have created two datasets, Twitter containing 2 million
tweets and Reddit dataset of 7.5 million sentences. The datasets have been
used in research on style transfer i.e. adjusting or removing the
offensiveness in a sentence.

Madaan et al. [100] use the Enron corpus [91] to create Politeness
dataset999https://github.com/tag-and-generate/politeness-dataset, last
visited: 28.08.2020 of 270,000 emails, labeled as polite or impolite. The
potential use of this dataset would be to convert a neutral sentence into a
polite sentence.

Cao et al. [101] have created the Expertise
dataset101010https://srhthu.github.io/expertise-style-transfer/#disclaimer,
last visited: 26.02.2021 based on the Mericks Manuals that is suitable for
transfer of style between expert and layman medical language style. The
dataset is composed of sentences from the domain of medical science: 130,349
sentences were written in medical expert style and 114,674 sentences in layman
style. A small subset of the dataset contains aligned sentence pairs in both
styles.

Dataset Name | Year | Parallel (Y/N) | Type of Text Samples | Number of Samples | Labels for Style | References
---|---|---|---|---|---|---
Enron corpus [18, 91] | 2004 | Y | emails | 600K |  informal formal | [18]
Shakespeare [89, 90] | 2012 | Y | sentences | 21K |  shakespearean english contemporary english | [33]
Gigaword [97, 98] | 2012 | N | news articles | 4M | seven publishers | [30]
SST [95] | 2013 | N | reviews | 9.6K |  positive negative | [31]
IMDB [96] | 2014 | N | reviews | 16K |  positive negative |  [17], [31], [34], [102]
Amazon [94] | 2016 | N | reviews | 1M |  positive negative |  [14], [15], [19] [22], [26], [27] [29], [103], [104]
Gender [93] | 2016 | N | reviews | $*$ |  female male |  [15], [21], [24]
Captions [92] | 2017 | Y | image captions | 7K |  factual romantic humorous |  [14], [15], [27]
GYAFC [53] | 2018 | Y | sentences | 110K |  informal formal |  [16], [18], [28], [105]
Paper-News Titles [19] | 2018 | N | titles | 200K |  paper news |  [19], [106]
Political slant [99] | 2018 | N | posts | 540K |  democratic republican |  [15], [21], [24], [25]
Twitter [20] | 2018 | N | tweets | 2M |  offensive non-offensive | [20]
Reddit [20] | 2018 | N | sentences | 7.5M |  offensive non-offensive |  [18], [20]
Yelp | 2020 | N | reviews | 8.6M |  positive negative |  [14], [15], [17], [21] [22], [23], [24], [25] [26], [27], [28], [29] [32], [33], [34], [35] [103], [105], [107] [102], [104], [106]
Politeness [100] | 2020 | N | emails | 270K |  neutral polite | [100]
Expertise [101] | 2020 | N | documents | 200K |  expertise laymen | [101]
$*$ The dataset is composed of reviews written by 432M users, however the
number of reviews is not specified by the authors.
Table 2:  A list of publicly available datasets for text style transfer.

###  3.3 Evaluation of Automatic Style Transfer

Evaluations of text style transfer face the longstanding challenges in the
field of natural language generation (NLG) [108]. In regard to text style
transfer, the objective of the evaluation is two-fold: 1) to measure how well
the meaning of the original sentence was preserved in the output (generated
sentence) and 2) to evaluate the quality of the style. Ideally, the goal is to
create a model that successfully modifies the style of a text, while its
meaning is preserved.

A different set of metrics have been used for evaluating both aspects. The
quality of content preservation is evaluated using evaluation metrics that
measure the extent to which the generated sentence matches human output, which
is used in other NLG tasks, including summarization [12], image captioning
[109] and machine translation [110]. A new set of metrics, specifically
tailored to measure the style strength, are proposed for measuring the quality
of generating a text in the target style.

####  3.3.1 Evaluation of the Quality of Semantic Content Preservation

Despite the criticism of using metrics based on language modeling and
similarity measures, a number of well-established metrics have been adopted
for measuring the quality of text generation.

Word overlap based metrics METEOR [111] and BLEU [112], were introduced for
the evaluation of machine translation, by computing a score that indicates the
similarity between the system output and one or more human-written reference
texts. METEOR [111] evaluates the generated sentence by aligning it to one or
more reference sentences. Alignments are based on exact, stem, synonym, and
paraphrase match between words and phrases. METEOR is calculated as a harmonic
mean of unigram precision and recall, with recall being weighted higher. BLEU
[112] measures how close a candidate sentence is to a reference sentence based
on matches of n-grams of a sentence to a reference one. NIST [113] is a
version of BLEU metric that values the less frequent n-grams more. BERTScore
[114] computes the cosine similarity between contextualized BERT [115] word
embeddings of the sentence being evaluated and a set of reference sentences.

ROUGE-L [116] is a recall-oriented metric established for the evaluation of
text summarization that applies the concept of the Longest Common Subsequence
(LCS). The intuition behind the LCS concept is that the longer the LCS between
two sentences, the more similar they are. The score is $1$ when the two
sentences are equal, and $0$ when there is nothing in common between them.

SARI [117] is a metric for text simplification that considers the number of
additions and deletions. It measures the goodness of words that are added,
deleted, and kept by the system. SARI first calculates precision and recall
for each operation (addition, keep, and deletion). The final value is an
average of these scores.

PINC [118] is a measure originally developed for evaluating paraphrasing. It
evaluates how much a generated sentence resembles a reference sentence i.e.
how many n-grams differ between the sentences. The final score is the
percentage of n-grams that appear in the generated sentence but not in the
reference. The novelty of paraphrases is greater as the value increases.

####  3.3.2 Quality of a Style

To evaluate the quality of generating a sentence in a target (output) style,
various researchers calculate accuracy with a pre-trained classifier [14, 15,
16, 17, 18, 19, 20, 21, 22, 23, 24, 26, 27, 28, 29, 31, 32, 33, 34, 35]. A
style quality is calculated as a percentage of generated sentences that were
labeled with the target style by the classifier. Higher value indicates better
style quality. Precision, recall, and F1-measure are also appropriate for the
evaluation of the quality of a style.

##  4 Deep Neural Networks for Text Generation

###  4.1 Recurrent Neural Networks

Recurrent Neural Networks (RNNs) [119] are a class of deep learning networks
designed for modeling sequential data. RNNs process the input sequence from
beginning to end (forward direction). Bidirectional Recurrent Neural Networks
(BiRNNs) [120] are composed of two unidirectional RNNs operating in both
directions (forward and backward).

Long Short-Term Memory Networks (LSTM) [121] are a specific type of RNN,
designed to learn long-range dependencies as well as to overcome the problem
of vanishing and exploding gradients. Gated Recurrent Units (GRU) [122] have
the same purpose as LSTM, but are known to be simpler and faster to train.

###  4.2 Convolutional Neural Networks

Convolutional Neural Networks (CNNs) [123] are a class of deep neural networks
developed for representing spatial features. Each neuron in CNNs is connected
with a local region of neurons in the previous layer. The parameters are known
as kernels that operate in two dimensions (2D convolution) over the spatial
data thus producing two-dimensional feature maps of input. CNNs are suitable
and are most commonly applied for image processing [124, 125]. Recently, CNNs
have been explored for modeling sequential data [126, 127, 128, 129, 130,
131], where the convolutional kernel operates in one dimension (1D
convolution).

###  4.3 Attention Mechanism

Attention is a deep learning technique inspired by human cognitive attention
that was introduced by Bahdanau et al. [110] to improve machine translation.
Attention is a technique, which computes a weighted sum of attention scores
assigned to the elements of the input sequence that help the decoder to attend
to certain elements of the input. There are various types of attention: dot-
product attention, multiplicative attention, additive attention, self
attention [132, 133, 134].

Self-attention is used for representation of a sequence while giving attention
to relevant parts of the same sequence. Performing self-attention multiple
times in parallel is defined as multi-head self-attention. Multi-head self-
attention combines information from different representation subspaces.
Transformer [134], a deep neural architecture proposed for language modeling
by multi-head self-attention, allows significantly more parallelization than
RNN. Various models have been built following the Transformer architecture:
BERT [115], DistilBERT [135], RoBERTa [136], GPT [137], GPT-2 [138], GPT-3
[139], etc.

Pointer network [140, 141] is a mechanism for ”pointing out” to relevant parts
of the input. In a pointer network, attention is used as a pointer for
selecting parts of the input sequence as members of the output sequence.

###  4.4 Encoder-decoder

Encoder-decoder (also referred to as sequence-to-sequence network) is a deep
neural network architecture for text generation [142]. The encoder learns to
generate a latent fixed-length vector representation of the input sentence.
The decoder learns to generate an output sentence by decoding the fixed-length
representation of the input sentence. The encoder and the decoder could be
RNNs, CNNs, MLPs, attention-based networks, or a combination.

Autoencoder (AE) [143] and Variational Autoencoder (VAE) [144] are deep
learning architectures intended for learning an internal representation of the
input. As in the encoder-decoder architecture, the encoder is a neural network
that produces fixed-length representation. The decoder learns to reconstruct
the input sentence based on the encoded representation. VAE is a generative
model that learns the distribution of the data with a stochastic variational
and learning algorithm. AE and VAE could be viewed as a specific type of
encoder-decoder architecture where the goal is to generate an encoded
representation of the input data.

Encoder-decoder network has been applied in natural language generation for a
number of tasks: machine translation [10, 11, 110, 145], text summarization
[146], question answering [147], etc.

###  4.5 Generative Adversarial Networks

Generative Adversarial Network (GAN) [148] is a deep neural network
architecture comprised of two networks – generator and discriminator. These
two networks are trained simultaneously in a two-player minimax game. The
generator network aims to learn the distribution of the training data and to
generate samples from the learned distribution. The discriminator network
determines whether a sample is from the data distribution or from the model
distribution, by maximizing the probability of assigning the correct label to
samples from the training data as well as from the generated data. The
objective of the generator is to generate samples that are indistinguishable
from the training samples, by maximizing the opposite objective of the
discriminator. GANs have been applied in many natural language generation
tasks [149, 150, 151] including machine translation [152], text summarization
[153] and question answering [154].

##  5 Methods for Style Transfer

In this section, the discussion of deep learning (DL) models that were used in
the research on text style transfer is presented. The vast majority of the
models are built upon the encoder-decoder architecture [14, 15, 16, 17, 18,
19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 103, 104], while in
another line of research, adversarial learning with GANs have been put forward
[32, 33, 34, 35, 102, 105, 106, 107].

A general encoder-decoder-based architecture for style transfer is depicted in
Figure 1. The encoder creates a latent representation of the input sentence
i.e., encodes the input sentence. The decoder generates the output sentence
conditioned on the latent representation, while the classifier determines the
style label of the output sentence. The classifier is an optional component in
style transfer models. If the style transfer model is based on GAN, the
encoder is referred to as generator and the classifier component as a
discriminator. Style embeddings have been introduced to assist in the encoding
of input sentence and/or generation of output sentence. Style embedding could
be added as input to the encoder [29, 31], decoder [14, 15, 18, 19, 23, 34,
103, 104], or both [17, 20, 32, 35].

Figure 1: General architecture of deep neural style transfer model.

###  5.1 Representation Learning

An encoder is applied to create a latent vector representation of the content
of a sentence. It transforms a sentence while preserving its semantic and
syntactic properties. The initial building block, the encoder in style
transfer models is usually a type of RNN. Two types of RNNs, LSTM [23, 26, 31,
35, 102, 103, 107] and GRU [14, 19, 20, 22, 25, 28, 32, 34], have been
employed to learn the representation of input sentences.

In the literature on text style transfer, two types of encoders can be
identified: shared and private encoders. When a shared encoder is used, the
parameters are shared across the sentences of the entire dataset, so the
encoder learns the style characteristics of the entire dataset. A private
encoder is used to learn style-specific characteristics since the parameters
are shared only across sentences of a specific style. Most of the style
transfer models have opted for a shared encoder. Zhang et al. [30] have
introduced a system that uses both, private and shared encoders. Each sentence
is passed through two GRU encoders, one private encoder for a particular style
and one encoder shared across all styles. Zhao et al. [33] proposed
decomposing each sentence into two latent representations by using two GRU
encoders, one for style representation, and the other for creating content
representation. In a model called StyIns [105], instead of learning style
embeddings from a single sentence, the generative flow techniques [155] have
been used to learn the stylistic properties from a set of sentences sharing
the same style i.e., style instances. Coupled with an attention-based decoder,
StyIns model yielded higher style accuracy while preserving the content of the
original sentence when evaluated on three style transfer tasks.

Motivated by the findings that architectures for machine translation preserve
the semantic meaning of a sentence, but not its stylistic properties [156],
Prabhumoye et al. [21, 24] have incorporated a machine translation-based model
in the first stage of style transfer i.e., for representation learning of
input sentences. While deep learning architectures for machine translation
have reached state-of-the-art performances for many languages, their
integration into deep learning pipelines for other tasks still face
challenges.

Various models for text style transfer employ variants of Transformer
architecture [16, 17, 18, 27, 29, 104], to benefit from the self-attention
mechanisms when learning the representation of the input sentence.

An additional step in the process of style transfer, related to detection and
removal of style markers from the input sentence, has been included in a
number of studies [14, 15, 23, 29]. Style markers are words that have the most
discriminative power for determining the style of a sentence. Models proposed
by Sudhakar et al. [15] do not use an encoder to create a latent
representation of the sentence. Instead, the input sentence is reduced by
removing the style markers and then it is passed directly to the decoder.

####  5.1.1 Detecting Style Markers

In most models for style transfer, the entire sentence is fed into the model
[16, 17, 18, 19, 20, 21, 22, 24, 25, 26, 27, 28, 30, 31, 32, 33, 34, 35]. Li
et al. [14], Sudhakar et al. [15], Zhang et al. [23] and Lee [29] argue that
style transfer can be accomplished by changing a few style markers. Based on
this idea, the input sentence is preprocessed in a way that style markers are
removed from it. The preprocessed sentence is then fed into the model as an
input sentence. It should be noted that detecting and removing style markers
has been proposed and evaluated only on the style transfer task of sentiment
modification.

Several approaches for detecting style markers have been proposed. Li et al.
[14] have used n-gram salience measure for identifying style markers. It
calculates the relative frequency of n-grams in sentences with a specific
style. An n-gram is considered to be style marker if its salience is above a
specific threshold. Zhang et al. [23] used attention weights [157] to detect
style markers. A word is defined as a style marker, if its attention weight is
greater than the average attention value. Sudhakar et al. [15] introduced an
importance score for each token in the input sentence, based on attention
scores of BERT [115] style classifier. Tokens with the highest importance
score represent style markers. Lee [29] proposed to identify style markers by
monitoring the change in probabilities of a style classifier. Important Score
(IS) of a token is defined as a difference between the probability of the
style conditioned on the entire sentence and the probability of the style
conditioned on a sentence without the specific token. Token is a style marker
if it has largest IS.

###  5.2 Sentence Generation

A crucial component of deep style transfer models is the part that generates
the output sentence based on the representation of the content of the original
sentence and the corresponding styles. We start our discussion with two models
for sentiment modification, proposed by Li et al. [14], that are often used as
baseline methods other research is compared to. The two models are not based
on deep learning, but they use rather simplistic methods of retrieval or word
swapping using two corpuses of sentences with positive and negative sentiment.
The RetrieveOnly model simply outputs the retrieved sentence from the corpus
that is most similar to the input sentence. A grammatically correct output
sentence is expected, although the content of the original sentence might not
be preserved.

The TemplateBased model removes the words identified as style markers from the
input sentence and replaces them with the style markers from the retrieved
corpus sentence that is most similar to the original. This is a naïve method
of word swapping based on the assumption that original style markers could be
replaced with words with the opposite sentiment if they appear in a similar
context (retrieved sentence). It is not surprising that very often the
generated output sentence appears to be grammatically incorrect.

The first deep learning models developed for text style transfer were inspired
by sequence-to-sequence systems for machine translation [110] and paraphrasing
[158, 159, 160]. An MT-based sequence-to-sequence model has been proposed for
transforming text from modern English to Shakespearean English [46]. The
output probability distribution is produced by a two-part decoder, consisting
of an attention-based LSTM decoder and a pointer network. The pointer network
was added to facilitate direct copying of the words from the input into the
output sequence. Pre-trained word embeddings using external sources, such as
dictionaries have been used to mitigate the problem of a limited amount of
parallel data. An interesting approach of harnessing rules for formality style
transfer using pre-trained GPT-2 Transformers was incorporated in several
models proposed by Wang et al. [161] to overcome the problem of small parallel
datasets.

We group the models for style transfer into three groups according to the
architectural blocks for generating the output sentence: simple reconstruction
models, models with style classifiers, and adversarial models. Simple
reconstruction models are trained to simply reconstruct the sentences based on
the reconstruction loss (called self-reconstruction). Models in the second
group, incorporate additional style classifier(s) to assist in the generation
of the output sentences, while models in the adversarial group are built upon
GAN architecture.

####  5.2.1 Simple Reconstruction Models

Most of the style transfer models generate the output sentence as a simple
“reconstruction” of the content of the input sentence by maximizing the
probability distribution of the next word in the output sequence conditioned
on the latent representation of the words in the input sentence. Style
transfer research that belongs to the group of simple reconstruction models
are presented in Table 3.

Two of the pioneering encoder-decoder models using simple reconstruction,
DeleteOnly and DeleteAndRetrieve [14] have been proposed by the authors
advocating style markers removal for the sentiment modification task. The
decoder of the DeleteOnly model generates the output sentence based on the
encodings of the input sentence and the target style (encoded by a separate
style encoder). The style of the retrieved corpus sentence that is most
similar to the input sentence is encoded in the DeleteAndRetrieve model to
condition the output sentence generation.

Sudhakar et al. [15] proposed two models, Blind Generative Style Transformer
(B-GST) and Guided Generative Style Transformer (G-GST), that follow the same
modeling approach as DeleteOnly and DeleteAndRetrieve [14], respectively.
However, both models incorporate Transformer-based decoders.

Lample et al. [103] have suggested using a back-translation technique instead
of adversarial training in their model MultipleAttrTransfer. By doing this,
the generated output by the model is also used as a training input example fed
into the encoder during “back-translation”. In terms of the objective function
being optimized, a so-called cycle reconstruction is added to the original
denoising auto-encoder. In addition, by using a temporal max-pooling layer on
top of the encoder i.e. latent representation pooling, the decoder has better
control over content preservation.

Shared-Private Encoder-Decoder (SHAPED) [30] is composed of multiple GRU
encoders and multiple GRU decoders to learn both general and style-specific
characteristics. One encoder and one decoder are shared across all styles in
addition to the private encoders and decoders for each style. The outputs of
both, private and shared encoder-decoders are concatenated and processed with
a multi-layer feed-forward network to generate the output sentence. When the
style of the input sentence is unknown, the sentence is fed into all private
encoders. Their outputs are concatenated and fed into a style classifier that
determines the style of the input sentence.

Zhang et al. [23] take on another approach to the task of sentiment
modification in their Sentiment-Memory based autoencoder (SMAE). A sentiment
classifier with self-attention mechanism is utilized to separate sentiment
from non-sentiment words, creating “sentiment memories” i.e. weighted matrices
of positive and negative sentiment word embeddings. During decoding, the
context of the input sentence is used to extract closely-related sentiment
entries from the sentiment memory matrix to condition the output sentence
generation

Model | Year | DL Architecture | Style Transfer Task(s) | Dataset(s)
---|---|---|---|---
RetrieveOnly [14] | 2018 | Baseline ML model | Sentiment Style Transfer | Yelp, Amazon, Captions
TemplateBased [14] | 2018 | Baseline ML model | Sentiment Style Transfer | Yelp, Amazon, Captions
DeleteOnly [14] | 2018 |  GRU encoder GRU decoder | Sentiment Style Transfer | Yelp, Amazon, Captions
DeleteAndRetrieve [14] | 2018 |  GRU encoder GRU decoder | Sentiment Style Transfer | Yelp, Amazon, Captions
SHAPED [30] | 2018 |  multiple GRU encoders multiple attentive GRU decoders | Genre Transfer | Gigaword
SMAE [23] | 2018 |  LSTM encoder LSTM decoder | Sentiment Style Transfer | Yelp
B-GST [15] | 2019 | BERT decoder |  Sentiment Style Transfer Personal Style Transfer |  Yelp, Amazon, Captions Political slant, Gender
G-GST [15] | 2019 | BERT decoder |  Sentiment Style Transfer Personal Style Transfer |  Yelp, Amazon, Captions Political slant, Gender
MultipleAttrTransfer [103] | 2019 |  LSTM encoder attentive LSTM decoder | Sentiment Style Transfer | Yelp, Amazon
Table 3:  Selection of style transfer research using simple reconstruction of
the input sentences in a new style.

####  5.2.2 Models with Style Classifier

A large group of models incorporate a style classifier to facilitate the
generation of the output sentence. The group of models shown in Table 4
incorporate a style classifier in the process of generating the output
sentence.

ControllableAttrTransfer [27] first embeds the input sentence with a
Transformer encoder, and then generates the output sentence with a Transformer
decoder. The encoded representation is additionally fed into a two-layer
linear classifier to provide a direction for editing the latent
representation, so that it conforms to the target style.

Back-translation for Style Transfer (BST) [21] is a back-translation based
model that learns to rephrase the sentence by reducing the effects of the
original style using English-to-French machine language translation model. A
style classifier is used to identify the style of the latent representation of
the back-translation model that later guides the generation of the output in
style-specific generators. BST includes multiple BiLSTM style-specific
decoders, one for each style. A style classifier is used to identify the style
of the latent representation of the back-translation model that later guides
the generation of the output sentence by the style-specific decoders. Multi-
lingual Back-translated Style Transfer (M-BST) and Multi-lingual Back-
translated Style Transfer + Feedback (M-BST+F) [24] are extensions of BST.
M-BST creates latent sentence representation with multilingual MT model, while
BST exploits monolingual MT model. In M-BST+F, a feedback-based loss function
was introduced to guide the decoder.

Neural Text Style Transfer (NTST) [20] and StableStyleTransformer [29] utilize
a CNN classifier to classify the style of the generated sentence. Unlike BST,
these models are composed of a single decoder and therefore the inclusion of a
style embedding as input to the decoder is needed to generate a sentence in
the desired style.

StyleTransformer [17] model uses a discriminator network as another
Transformer encoder to distinguish the styles of sentences. The authors have
experimented with two types of discriminator networks: a conditional
discriminator to confirm or not the input style, and a multi-class
discriminator that classifies a given sentence to one of the K style classes.
The training algorithm goes through two phases: one for training the
discriminator and the other is for training of the StyleTransformer network.
For better preservation of the content of the original style, a cycle
reconstruction loss is used as an objective function when training the model
to generate the original input sentence if the generated output sentence is
fed into the network.

Context-aware Style Transfer (CAST) [18] trains two separate decoders for each
sentence to ensure coherence with the adjacent context i.e., sentences in the
same paragraph. By doing this, the model was able to preserve the style-
independent content of the input sentence, while maintaining its consistency
with the surrounding text.

To make use of parallel dataset when available, a bidirectional translation
loss was introduced in HybridST [16], which is a combination of: 1) the loss
of generating the output sentence given the input sentence and 2) the loss of
generating an input sentence given an output sentence. In Fine-Grained
Controlled Text Generation (FineGrainedCTGen) [26] additional Bag of Words
(BOW) component was added to enhance generation of specific words and to
preserve the content by minimizing the negative log probability of generating
BOW features for the output sentence.

In POS-LM [25], two additional components were added: Part of Speech (POS)
tagger and Language Model (LM). POS tagger assists in generating previously
determined nouns in the output sentence, while LM controls the perplexity of
the generated sentence. Zhou et al. [28] point out that training a model with
reconstruction and classification loss might result in extremely short
sentences that might match the target style, but would fail to preserve the
original meaning. They proposed the CP-LM model that incorporates two losses:
1) content preservation loss to force the word embedding representation of the
input and output sentences to be close, by minimizing the difference of their
embedding representations and 2) fluency modeling loss to ensure that the
output sentences are fluent by minimizing the negative log probability of
generated words, similar to the bidirectional translation loss used in
HybridST.

Kim and Sohn [104] argue that performing sentence reconstruction and style
control in a single task increases the complexity of the model. Their proposed
model AdaptiveStyleEmbedding consists of two modules, one for each task: 1) a
style module that learns the style embeddings using a style classifier, and 2)
an autoencoder that generates the output sentence conditioned on the combined
vector of latent representation of the input sentence and the learned style
embedding.

Model | Year | Architecture | Style Transfer Task(s) | Dataset(s)
---|---|---|---|---
BST [21] | 2018 |  MT encoder multiple BiLSTM decoders CNN classifier |  Sentiment Style Transfer Personal Style Transfer | Yelp, Gender, Political slant
M-BST [24] | 2018 |  multilingual MT encoder multiple BiLSTM decoders CNN classifier |  Sentiment Style Transfer Personal Style Transfer | Yelp, Gender, Political slant
M-BST+F [24] | 2018 |  multilingual MT encoder multiple BiLSTM decoders CNN classifier |  Sentiment Style Transfer Personal Style Transfer | Yelp, Gender, Political slant
NTST [20] | 2018 |  GRU encoder attentive GRU decoder CNN classifier |  Transferring Offensive to Non-offensive Text | Reddit, Twitter
POS-LM [25] | 2018 |  GRU encoder attentive GRU decoder CNN classifier |  Sentiment Style Transfer Personal Style Transfer | Yelp, Political slant
HybridST [16] | 2019 |  Transformer encoder Transformer decoder CNN classifier | Formality Transfer | GYAFC
StyleTransformer [17] | 2019 |  Transformer encoder Transformer decoder Transformer discriminator | Sentiment Style Transfer | Yelp
ControllableAttrTransfer [27] | 2019 |  Transformer encoder Transformer decoder MLP classifier | Sentiment Style Transfer | Yelp, Amazon, Captions
CP-LM [28] | 2020 |  GRU encoder attentive GRU decoder CNN classifier |  Sentiment Style Transfer Formality Transfer | Yelp, GYAFC
CAST [18] | 2020 |  Transformer encoder Transformer decoder CNN classifier |  Formality Transfer Transferring Offensive to Non-offensive Text | GYAFC, Enron corpus, Reddit
StableStyleTransformer [29] | 2020 |  Transformer encoder Transformer decoder CNN classifier | Sentiment Style Transfer | Yelp, Amazon
FineGrainedCTGen [26] | 2020 |  LSTM encoder LSTM decoder MLP classifier | Sentiment Style Transfer | Yelp, Amazon
AdaptiveStyleEmbedding [104] | 2020 |  Transformer encoder Transformer decoder MLP classifier | Sentiment Style Transfer | Yelp, Amazon
Table 4:  Selection of style transfer research using a classifier to assist in
the generation of sentences.

####  5.2.3 Adversarial Models

Several style transfer models incorporate style discriminators, which have a
similar role as the discriminator in the GAN architecture. The research
studies using the adversarial framework for generating the output sentence are
listed in Table 5.

AttrControl [34] is an encoder-decoder based model that employs a Projection
Discriminator [162] to generate realistic and style compatible sentences. The
discriminator determines whether the generated sentence is real or fake, based
on a style embedding and output sentence obtained by a GRU decoder. Controlled
Text Generation (CTGen) [31] is built upon VAE architecture. Additional CNN
discriminators were included to assist the generation process. The generator
and the discriminators provide feedback to each other in a collaborative
manner with the wake-sleep procedure [163].

Aligned Autoencoder (AAE) [32] incorporates a feed-forward discriminator to
align both, posterior probability distributions learned with encoders for each
style (input and target style). Cross-Aligned Autoencoder (CAAE) [32]
incorporates two CNN discriminators for the same purpose. Assuming the
transfer is between two styles (style $s_{i}$ and style $s_{t}$), one
discriminator learns to distinguish between a real sentence with style $s_{i}$
and generated sentence with style $s_{t}$, while the other discriminator
learns to distinguish between a real sentence with style $s_{t}$ and generated
sentence with style $s_{i}$.

MultiDecoder and StyleEmbedding [19], incorporate two multi-layer classifiers
to classify the style of the input sentence given the representation learned
by the GRU encoder, by 1) maximizing the probability of correctly predicting
style labels, and 2) maximizing the entropy of the predicted style labels.
StyleEmbedding uses an additional embedded representation of the target style
to the GRU decoder to generate an output sentence, while MultiDecoder is
composed of multiple GRU decoders (one for each style) to generate a sentence
in the target style.

The research study presented by John et al. [22] tackled on somewhat divisive
topic of the feasibility of disentangling the content from the style in the
latent space. Deterministic AutoEncoder (DAE) and Variational AutoEncoder
(VAE) have been used in their models for the task of sentiment style transfer.
A number of content-oriented and style-oriented reconstruction and adversarial
losses have been proposed to afford the separation of the latent spaces. The
content and style information have been approximated by the bag-of-words (BOW)
features. Two classifiers have been used: one for detecting the style and the
other over the BOW content vocabulary.

Cycle-consistent Adversarial Autoencoder (CAE) [107] is a three-component
network consisting of LSTM autoencoder for representing sentences in different
styles, adversarial style transfer network, and a novel cycle-consistent
constraint. The cycle-consistent reconstruction imposes constraint on the
latent representation collectively learned by the LSTM autoencoder and
adversarial style network. The results of the conducted ablation study show
that the cycle-constraint was instrumental in content preservation during
sentiment style transfer. In a similar way, Dual-Generator Network for Text
Style Transfer (DGST) [102] learns to generate sentences in a target style in
a cyclic process. However, this model does not rely upon discriminators.
Instead, it applies neighborhood sampling to introduce noise to each sentence.
FM-GAN [35] is trained with Feature Mover’s Distance instead of traditional
loss for adversarial learning.

Zhao et al. [33] point out that the generated sentence may not necessarily
capture the target style by training with an objective function that includes
only reconstruction and adversarial loss. Their model StyleDiscrepancy
incorporates an additional discriminator to determine whether a given sentence
has the target style with a loss function called style discrepancy. They also
apply cycle consistency as in StyleTransformer [17] and CAST [18] models.
StyIns [105] model incorporates adversarial style loss to ensure better style
supervision during generation. Similar to StyleTransformer [17], a multi-class
discriminator is applied to determine the style of the generated sentence.

A new framework named Pre-train and Plug-in Variational Autoencoder (PPVAE)
was proposed by Duan et al. [106] with a realistic system in mind that can
mitigate the problem of starting from scratch whenever we need to learn a new
style. The framework PPVAE is composed of two variational autoencoders: the
PretrainVAE, which learns to represent and reconstruct a sentence in its
original style and the PluginVAE, which learns the conditional latent space
for each style. The role of PluginVAE as a lightweight easily-trained network
is to transform the conditional “style-specific” latent space into the global
latent space learned by PretrainVAE and vice versa.

Model | Year | Architecture | Style Transfer Tasks(s) | Dataset(s)
---|---|---|---|---
AAE [32] | 2017 |  GRU encoder GRU generator MLP discriminator | Sentiment Style Transfer | Yelp
CAAE [32] | 2017 |  GRU encoder GRU generator CNN discriminator | Sentiment Style Transfer | Yelp
CTGen [31] | 2017 |  LSTM encoder LSTM generator CNN discriminator | Sentiment Style Transfer | SST, IMDB
AttrControl [34] | 2018 |  GRU encoder GRU decoder Projection discriminator | Sentiment Style Transfer | Yelp, IMDB
StyleDiscrepancy [33] | 2018 |  two GRU encoders GRU generator CNN discriminator |  Sentiment Style Transfer Shakespearean Style Transfer | Yelp, Shakespeare
FM-GAN [35] | 2018 |  LSTM encoder LSTM generator MLP classifier | Sentiment Style Transfer | Yelp
StyleEmbedding [19] | 2018 |  GRU encoder GRU decoder MLP classifier |  Genre Transfer Sentiment Style Transfer | Paper-News Titles, Amazon
MultiDecoder [19] | 2018 |  GRU encoder multiple GRU decoders MLP classifier |  Genre Transfer Sentiment Style Transfer | Paper-News Titles, Amazon
DAE [22] | 2019 |  GRU encoder GRU decoder CNN classifier | Sentiment Style Transfer | Yelp, Amazon
VAE [22] | 2019 |  GRU encoder GRU decoder CNN classifier | Sentiment Style Transfer | Yelp, Amazon
StyIns [105] | 2020 |  BiLSTM encoder attentive RNN decoder CNN discriminator |  Sentiment Style Transfer Formality Style Transfer | Yelp, GYAFC
CAE [107] | 2020 |  LSTM encoders LSTM generators MLP discriminators | Sentiment Style Transfer | Yelp
DGST [102] | 2020 |  BiLSTM encoders BiLSTM generators | Sentiment Style Transfer | Yelp, IMDB
PPVAE [106] | 2020 |  BiGRU and MLP encoders Transformer and MLP decoders MLP discriminator |  Genre Transfer Sentiment Style Transfer | Yelp, News Titles
Table 5:  Selection of style transfer research using adversarial learning for
generating sentences in a new style.

##  6 Challenges and Directions for Future Research

Before concluding this paper, it is important to emphasize the challenges the
task of text style transfer faces. Research performed thus far offers a
promising perspective but also points to unexplored avenues that require
further attention.

###  6.1 Datasets

Advancing the state-of-the-art systems for style transfer is dependent on the
quality and quantity of the available datasets. They provide the necessary
support for advocating the use of deep learning techniques. Due to the
diversity of style categories, the ambiguity of the stylistic properties being
modeled and the costs of labeling, creation of benchmarking datasets is still
non-trivial. Publicly available datasets for style transfer vary depending on
the type of style they contain (e.g., sentiment, formality, politeness) they
contain, the format of text samples, and the procedure used for content
validation and labeling (e.g., crowdsourcing, experts). While parallel corpus
datasets would be desirable for style transfer tasks, it is often unrealistic
to obtain and label large datasets, so the rise in unsupervised deep style
transfer methods is not surprising.

###  6.2 Deep Learning

Heralded for their capabilities for automated feature learning and complex
pattern recognition from vast quantities of big data, deep learning techniques
have been at the frontier of innovations for decades now. The aim of this
survey was to highlight the importance and to demonstrate the suitability of
deep learning for the task at hand.

The focus of the research has shifted from feature extraction to model-free
machine learning. The knowledge is unearthed directly from abundant data
without the need for domain expertise, hand-crafted feature extraction, or
data labeling. Deep learning is currently being preferred choice for text
style transfer and have proved to be more scalable, robust, and superior in
performance on various style transfer tasks. Existing deep neural
architectures have been adapted for both stages in the process: the
representation learning of the input sentence whose style needs to be changed
and the generation of the output sentence in a new style.

The technological solutions for output sentence generation were the criteria
for clustering the style transfer research discussed in the survey into three
groups. There are strengths and limitations associated with deep learning.
Their dependency on large quantities of data and the complexity of the neural
models are related to the problems of overfitting from training data and
inability to generalize well.

###  6.3 Deep Style Transfer

Points of particular interest for future directions in style transfer are
expected in the following areas:

####  6.3.1 Style-content Disentanglement

The discussions that extend across several studies are the challenges in
disentanglement of content and style in text. Style is indeed inseparably
woven into spoken and written language. Some of the past research studies have
advocated that the success of the style transfer task depends on the clear
separation between the semantic content from the stylistic properties.
Approaches being proposed include: partitioning of the latent space into
content and style subspaces [19, 22, 31, 32, 33, 105], removal of style
markers [14, 15, 23, 29], and use of back-translation for reducing the effects
of the style of the input sentence [21, 24].

Across recent studies, the majority of researchers clearly reject the
necessity for disentangling content from style; a target that is difficult to
reach even by adversarial training. A number of methods have been suggested
that are much more effective on style transfer tasks without the need for
disentanglement. The use of back-translation technique [17, 18, 20, 103],
latent representation editing [27], independent modules for style control and
sentence reconstruction [104], and a cycle-consistent reconstruction [107] are
some of the approaches put forward. Given the promising success of
adversarial-based method for style transfer task, there is evidence that
further advancing the proposed adversarial architectures is worth exploring.
Surely, these divisive views need to be further explored to consolidate the
views across different style transfer tasks.

####  6.3.2 Content Preservation vs. Style Strength Trade-off

A key challenge for the methods for style transfer in text is identifying
strategies that can effectively balance the trade-off between preserving the
original content and changing the style of a given sentence [14, 18, 26, 29,
102, 103, 104, 105]. The balance is problematic because the precise nature of
the interdependence between style-free and style-dependent content is not
clearly defined. The unavoidable trade-off between content preservation and
style strength in the proposed models is shared among researchers in the
field. Furthermore, previous studies suggest that some technological remedies
are at odds with one another [17, 102, 107].

####  6.3.3 Interpretability

Deep neural networks are sometimes criticized for being black-box models,
their structure and output not intelligible enough to associate causes with
effects. In the field of natural language processing, we would want to
interpret the model in a way that we can identify the useful patterns and
features that contribute more to a better understanding and generation of
text. Making attempts to understand how and how well different deep network
components have been done in machine vision by dissecting GANs [164].

In the realm of natural language understanding, a number of perspectives has
been fitted under the umbrella of style, from genre and formality to personal
style and sentiment. This is an area where interdisciplinary endeavor of
theoretical and empirical research should complement each other. The topic of
stylistic language variations has a rich history in sociolinguistic theory [4,
5, 6], although reciprocal contributions from both sides are expected to shed
light on the multifaceted nature of style, provide guidance to the modeling
efforts and improve the interpretation of the empirical results.

####  6.3.4 Transfer Learning

Training models is a computationally intensive and time-consuming process. In
the field of computer vision and natural language understanding, transfer
learning has been used to both, to speed up the process and improve model
performance. General semantic patterns are learned during pre-training and can
be ”transferred” to new tasks. By fine-tuning, such additional semantic
information can be ”transferred” into the learned representation. Currently,
the number of research studies using transfer learning for style transfer is
still limited. However, as popularity and attention to the topic increases,
the idea of pre-training, multi-task training, and then fine-tuning can be
more promising.

####  6.3.5 Ethical Considerations

The far-reaching consequences of any research should engage the community in
ethical discussions on potential malicious abuse as well as the impact a
technology has on transforming many aspects of human life. The need for
reflection is further amplified by the increased reliance of current machine
learning technologies on abundant data that is scraped from social networks.

####  6.3.6 Deep Reinforcement Learning

The new directions towards human-like understanding, such as few-shot learning
[165], inductive learning [166] or lifelong learning [167] are promising areas
of machine learning. A vision of designing machines that learn like humans
from experience based on a limited number of training examples is yet to be
reached [168]. Notably, deep reinforcement learning has recently been
revisited by researchers in many fields including our own [169, 170, 171].

The advances reviewed in this survey should be of interest to researchers not
only limited to style transfer, but also to other related fields, such as
language generation, summarization, question answering and dialogue.

##  7 Conclusion

Recent advancements in text style transfer using deep learning have been the
primary motivation to carry out the survey presented in this paper. A
systematic review of state-of-the-art research highlights the trends that
appeared to extend across research studies as well as differences and
variations in style transfer methodologies using deep learning. In particular,
this review examines how encoder-decoder-based architectures are still
dominating the field, with a more recent move toward adversarial learning
using Generative Adversarial Networks. While it appears that a choice of one
deep neural network over another is style-independent, balancing the trade-
offs between the complexity of a model and the expected performance gains
added by auxiliary components (e.g., classifier, discriminator) are consistent
challenges faced by researchers. The review is structured around the key
stages in style transfer process and the methodological differences adopted by
researchers for each stage.

It is our hope that the review would serve as a guideline for future studies
that are built on the best practices of past research as well as the new
direction that can enrich the field. Notably, successful studies of
generalizing results across style transfer tasks are rarely reported. Transfer
learning and multitask learning studies are the opportunities that could make
further progress possible. Interpretability is a recurring challenge that is
shared across respective fields – a better understanding of what stylistic
indicators are captured and learned by neural models might elucidate the
nature of stylistic variations in language.

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