# Transductive Learning for Unsupervised Text Style Transfer

Fei Xiao1,4, Liang Pang1∗, Yanyan Lan3∗, Yan Wang5, Huawei Shen1,4, Xueqi
Cheng2,4
1Data Intelligence System Research Center
and 2CAS Key Lab of Network Data Science and Technology,
Institute of Computing Technology, Chinese Academy of Sciences
3Institute for AI Industry Research, Tsinghua University
4University of Chinese Academy of Sciences 5Tencent AI Lab
{xiaofei19s, pangliang, shenhuawei<EMAIL_ADDRESS>
<EMAIL_ADDRESS><EMAIL_ADDRESS>
###### Abstract

Unsupervised style transfer models are mainly based on an inductive learning
approach, which represents the style as embeddings, decoder parameters, or
discriminator parameters and directly applies these general rules to the test
cases. However, the lacking of parallel corpus hinders the ability of these
inductive learning methods on this task. As a result, it is likely to cause
severe inconsistent style expressions, like the salad is rude. To tackle this
problem, we propose a novel transductive learning approach in this paper,
based on a retrieval-based context-aware style representation. Specifically,
an attentional encoder-decoder with a retriever framework is utilized. It
involves top-$K$ relevant sentences in the target style in the transfer
process. In this way, we can learn a context-aware style embedding to
alleviate the above inconsistency problem. In this paper, both sparse (BM25)
and dense retrieval functions (MIPS) are used, and two objective functions are
designed to facilitate joint learning. Experimental results show that our
method outperforms several strong baselines. The proposed transductive
learning approach is general and effective to the task of unsupervised style
transfer, and we will apply it to the other two typical methods in the future.
††footnotetext: *Corresponding Author

##  1 Introduction

Text style transfer is an essential topic of natural language generation,
which is widely used in many tasks such as sentiment transfer (Hu et al.,
2017; Shen et al., 2017), dialogue generation (Zhou et al., 2018; Niu and
Bansal, 2018; Su et al., 2021), and text formalization (Jain et al., 2019).
The target is to change the style of the text while retaining style-
independent content. As it is usually hard to obtain large parallel corpora
with the same content and different styles, unsupervised text style transfer
becomes a hot yet challenging research topic in recent years.

Figure 1:  Illustration of the inconsistency problem and the idea of our
transductive learning approach.

Most existing methods in this area try to find the general style transfer
rules with an inductive learning paradigm, where style is represented as a
specific form, e.g., embeddings, decoder parameters, or classifier parameters.
For example, embedding based methods (Shen et al., 2017; Lample et al., 2019;
John et al., 2019; Dai et al., 2019; Yi et al., 2020; He et al., 2020) utilize
a highly generalized style embedding to replace the original sentence style
and direct the generation process. Decoder based methods (Prabhumoye et al.,
2018; Fu et al., 2018; Luo et al., 2019; Gong et al., 2019; Krishna et al.,
2020) use multiple decoders for generation, where each encoder corresponds to
an independent style. Classifier based methods (Wang et al., 2019; Liu et al.,
2020; Mai et al., 2020) employ the gradient of a pre-trained style classifier
to edit the latent representation of the target text.

It has been well accepted that inductive learning methods have the ability to
work well when there are numerous supervised labels. However, in the case of
unsupervised style transfer, we are just given corpora with different styles
without knowing the parallel relation, i.e. supervision label for this task.
As a result, the inductive learning methods fail to produce an accurate style
transfer rule, leading to generating some severe inconsistent texts, such as ‘
_the salad is rude_ ’, as shown in Figure 1. The underlying reason for this
phenomenon is that a perfect style is usually highly dependent on the context,
e.g. ‘ _terrible_ ’ for ‘ _the salad_ ’ and ’ _rude_ ’ for ’ _a person_ ’.
Without a large scale of parallel data, it is difficult to learn a general
style transfer rule working for various contexts.

Inspired by the idea of transductive learning (Vapnik, 1998) and some
successful historical examples, such as Transductive SVM (Joachims et al.,
1999), we propose to introduce transductive learning to the area of
unsupervised text style transfer. Specifically, transduction learning
reasoning from specific cases to specific cases, which avoids learning a
general rule to represent the style. For example, once we get a reference
sentence ‘ _the sandwiches are terrible_ ’ with negative emotion, a
transductive learning method may connect the two sentences by the two kinds of
food, e.g. ‘ _salad_ ’ and ‘ _sandwiches_ ’, then use ‘ _terrible_ ’ to
express the negative emotion for the food ’ _salad_ ’.

From the above discussion, we can see that there are two challenges in
applying transductive learning to unsupervised text style transfer: 1) how to
find specific samples that are beneficial for the style transfer of the
current text; 2) how to use the style expressions in these samples to complete
the style transfer process. To tackle these two challenges, we propose a novel
TranSductive Style Transfer (TSST) model. In TSST, a retriever is employed to
obtain the required similar samples, which tackles the first challenge. An
attention-based encoder-decoder framework is then utilized to combine the
specific samples to tackle the second challenge. Specifically, TSST first
encodes the original text to a contextual representation and a style-
independent embedding. Then either sparse (BM25) or dense retrieval functions
(MIPS) are used to find the top-$K$ samples in the target style corpus, which
are encoded by the same encoder. After that, a recurrent decoder is utilized
to generate transfer text word by word based on the representation of those
retrieved samples, contextual representation, and the representations in the
last step. To jointly learn the dense retriever, encoder, and decoder, two
kinds of objective functions are used in this paper, i.e. retrieval loss and
bag-of-words loss.

In summary, our contributions are as follows:

  * •

Facing the inconsistency problem in unsupervised style transfer, we propose a
novel transductive learning approach, which avoids learning a general rule but
relies on specific samples to complete the style transfer process.

  * •

We design a TranSductive Style Transfer (TSST) model, which employs a
retriever to involve highly related samples to guide the learning of the
target style.

  * •

Experiments on two benchmark datasets show that TSST alleviates the
inconsistency problem and achieves competitive results against traditional
baselines. Our code is available at https://github.com/xiaofei05/TSST.

##  2 Related Work

Previous unsupervised text style transfer methods can be divided into three
categories according to the way they control the text style, e.g. embedding
based method, decoder based method, and classifier based method.

The embedding based methods assign a separated embedding for each style to
control the style of generated text. Early work tries to disentangle the
content and style in the text. They first implicitly eliminate the original
style information from the text representation using adversarial training
(Shen et al., 2017; Fu et al., 2018; John et al., 2019) or explicitly delete
style-related words (Li et al., 2018; Sudhakar et al., 2019; Wu et al., 2019b;
Malmi et al., 2020). Then, decode or rewrite the style-independent content
with the target style embedding. As a complete disentanglement is unreachable
and damaged the fluency of the text, recent approaches (Lample et al., 2019;
Dai et al., 2019; Yi et al., 2020; Zhou et al., 2020; He et al., 2020)
directly feed original text representation and a separated learned style
embedding to a stronger generator, e.g., the attention-based sequence-to-
sequence model or Transformer to obtain the style transferred text.

The decoder based methods build a decoder for each style or transfer
direction, where the style is implicitly represented as the parameters in the
corresponding decoder. The former schema built an independent decoder for each
style, which first disentangled the style-irrelevant content from the text and
then applied the corresponding decoder to generate sentences with the target
style (Fu et al., 2018; Xu et al., 2018; Prabhumoye et al., 2018; Krishna et
al., 2020). The latter built for each transfer direction, which often regarded
style transfer as a translation task (Zhang et al., 2018; Gong et al., 2019;
Luo et al., 2019; Jin et al., 2019; Wu et al., 2019a; Li et al., 2020). This
paradigm reduced the complexity of the learning of style to a certain extent
but consumed more resources. It is worth mentioning that the boundaries of
embedding controlled and decoder controlled methods are sometimes not very
clear, and many studies (Fu et al., 2018; He et al., 2020) consider that they
are alternative.

The classifier based methods convert the style by manipulating the latent
representation of the text according to a pre-trained classifier. Wang et al.
(2019) and Liu et al. (2020) mapped the input sentence into a latent
representation, and trained classifiers on this latent space. The latent
representation would be edited based on the gradients of the classifier until
the predicted style changed; after that, the decoder took the modified
representation to generate the desired style sentence. Mai et al. (2020)
further expanded this framework to a plug and play scene. Although it has
remarkable style accuracy, this method can hardly guarantee content
preservation due to the concrete output sharply changes with the latent
representation.

We can see that all of these existing methods belong to the inductive learning
approach, because they aim to learn a general style transfer rule from the
training data, and then apply the rule to the test cases. Due to the lack of
parallel corpus for supervision, this inductive learning approach fails to
learn an accurate style representation application for various contexts, and
may cause some severe inconsistency problems as illustrated before.

Figure 2:  Illustration of our TSST model for binary style transfer as an
example.

##  3 Transductive Style Transfer

Firstly, we introduce some notations. Consider the unsupervised text style
transfer task with $M$ styles, its training set is composed of $M$ single-
style subsets $\\{D_{i}\\}_{i=1}^{M}$. For an arbitrary input text $x$ in a
subset and the target style $s_{j}$, the goal of text style transfer is to
generate a new sentence $y$ which represents the style $s_{j}$ while keeping
the style-independent content of $x$ as much as possible.

To tackle the aforementioned inconsistency problem, we propose to utilize
transductive learning to obtain a context-aware style representation for the
style transfer process. Specifically, our proposed transductive style
transfer(TSST) model consists of three modules, encoder, retriever, and
decoder, as described in Figure 2.

###  3.1 Encoder

The goal of the encoder is to map the input sentence into hidden
representations, to facilitate the following retrieval and generation process.
Given the input sentence $x=(w_{1},w_{2},\dots,w_{n})$, the output of the
encoder is a sequence of hidden states,

$\bm{H}^{enc}=\textrm{Encoder}(x),$ (1)

where
$\bm{H}^{enc}=[\bm{h}^{enc}_{1},\bm{h}^{enc}_{2},\dots,\bm{h}^{enc}_{n}]^{\mathrm{T}}\in\mathbb{R}^{n\times
d}$, and $d$ is the dimension of the hidden state. Please note that the
encoder in our model is very general, and different encoding techniques can be
used. Specially, we employ a bidirectional LSTM in our experiments.

###  3.2 Retriever

The retriever module is introduced to involve the top-$K$ relevant texts in
the target style training subset, to facilitate the transductive learning
process. In this paper, we adopt both sparse and dense retrieval functions in
the retriever.

#### Sparse Retriever (BM25)

BM25 (Robertson and Zaragoza, 2009) is the most famous sparse retrieval
function, which has been widely used in information retrieval.

$\textrm{BM25}(\bm{q},\bm{d})=\sum_{w\in\bm{q}}\frac{\textrm{IDF}(w)\cdot
f(w,\bm{d})\cdot(k_{1}+1)}{f(w,\bm{d})+k_{1}\cdot(1-b+\frac{b\cdot|\bm{d}|}{\text{avgdl}})}$
(2)

where $k_{1}$ and $b$ are the hyper parameters, $f(w,\mathbf{d})$ represents
term frequency of $w$ in document $\mathbf{d}$, $\textrm{IDF}(w)$ represents
inverse document frequency of $w$, $|\mathbf{d}|$ denotes the document length
and avgdl denotes the averaged document length.

After that, the retrieved $K$ texts $\\{u_{i}\\}_{i=1}^{K}$ are mapped to
latent representations
$\bm{U}=[\bm{u_{1}},\bm{u_{2}},\dots,\bm{u_{K}}]^{\textrm{T}}\in\mathbb{R}^{K\times
d}$ by the above encoder, where $\bm{u_{i}}$ is the final hidden states for
$u_{i}$.

#### Dense Retriever (MIPS)

A term-based sparse retrieval would have difficulty retrieving such a semantic
context, which is essential for style transfer. Recently, dense retrieval
methods and their efficient implementation of maximum inner product search
(MIPS) (Shrivastava and Li, 2014; Guo et al., 2016; Cai et al., 2021) have
been proposed to capture the semantic. For a dense retriever, style-
independent text embedding is crucial because we rely on this embedding to
find similar samples in a different style subset. To this end, the style-
independent embedding $\bm{q}(x)$ of the text $x$ can be represented as a
linear combination of the hidden states $\bm{h}^{enc}_{i}$:

$\bm{q}(x)=\textrm{Softmax}([\alpha(w_{1}),\dots,\alpha(w_{n})])\cdot\bm{H}^{enc},$
(3)

where the parameter $\alpha(w_{i})$ is the weight for each word $w_{i}$. They
are initialized as $\alpha(w_{i})=1-\sum_{j}|f_{s_{j}}(w_{i})-1/M|$, where
$f_{s_{j}}(w_{i})$ is defined as the count of $w_{i}$ in the subset of style
$s_{j}$ across its total count in the whole dataset. This initialization
assigns a small weight to the discriminative words for each style, whose
frequencies in different style subsets vary significantly. Consequently, the
embeddings will focus on style-independent words, and help learn the style-
independent embeddings.

Based on the text embeddings, the dense retrieval approach is used to retrieve
the top-$K$ similar sentences in the target style training subset, where the
cosine similarity function is used to measure the similarity. Note that
computing text embeddings in the whole training set are time-consuming, so we
pre-compute the text embeddings at the beginning and update them after certain
training iterations, as inspired by Guu et al. (2020).

After that, the same encoder is employed to obtain the latent representations
of the top-$K$ texts, i.e.
$\bm{U}=[\bm{u_{1}},\bm{u_{2}},\dots,\bm{u_{K}}]^{\mathrm{T}}$, as did in the
sparse retriever.

###  3.3 Decoder

The decoder is used to generated the transferred text word by word. For each
step, the inputs of the decoder are composed of three parts: 1) output of the
previous step $\hat{y}_{t-1}$, 2) hidden states of $x$, i.e. $\bm{c}_{t}^{h}$,
and 3) latent representations of retrieved samples, i.e. $\bm{c}_{t}^{u}$. The
generated text $\hat{y}=\\{\hat{y}_{1},\hat{y}_{2},\dots,\hat{y}_{m}\\}$, is
obtained by the following equations:

$\displaystyle\bm{h}^{dec}_{t}=\textrm{Decoder}(\hat{y}_{t-1},\bm{c}_{t}^{h},\bm{c}_{t}^{u}),$
(4)
$\displaystyle\textrm{P}(\hat{y}_{t}|\hat{y}_{<t},\bm{H}^{enc},\bm{U};\bm{\theta})=\textrm{softmax}(\bm{o}_{\hat{y}_{t}}\bm{h}^{dec}_{t}),$

where $\bm{o}_{\hat{y}_{t}}$ is the parameters to obtain the predicted
probability on the word $\hat{y}_{t}$, and $\bm{c}_{t}^{h},\bm{c}_{t}^{u}$
indicates the attended input sentence and retrieved samples, respectively.
$\bm{c}_{t}^{h}$ and $\bm{c}_{t}^{u}$ are calculated by two attention modules
with different parameters:

$\displaystyle\bm{c}_{t}^{h}=\textrm{Attention}(\bm{h}^{dec}_{t-1},\bm{H}^{enc}),$
(5)
$\displaystyle\bm{c}_{t}^{u}=\textrm{Attention}(\bm{h}^{dec}_{t-1}\bm{W}_{h},\bm{U}\bm{W}_{u}),$

where $\bm{W}_{h}\in\mathbb{R}^{d_{dec}\times
d_{dec}},\bm{W}_{u}\in\mathbb{R}^{d\times d}$ and $d_{dec}$ is the dimension
of the decoder’s hidden state. The attention module is standard (Bahdanau et
al., 2015).

$\displaystyle\textrm{Attention}(\bm{h},\bm{H})=\sum_{j=1}^{n}\frac{exp(e_{ij})\bm{H}_{j}}{\sum_{k=1}^{n}exp(e_{ik})},$
(6) $\displaystyle
e_{ij}=\bm{v}^{T}\tanh(\bm{W}_{d}\bm{h}+\bm{W}_{e}\bm{H}_{j}),$

where $\bm{v},\bm{W}_{e},\bm{W}_{d}$ are parameters.

Similar to the encoder, various decoding techniques could be used in this
step, and we adopt LSTM in our experiments.

###  3.4 Learning Objectives

Except for the three widely used losses in previous style transfer works, i.e.
the reconstruction loss, the cycle reconstruction loss, and the adversarial
style loss, we also introduce two more losses related to the retriever, i.e.
the retrieval loss and the Bag-of-Word loss. Therefore, for a given input $x$,
its style $s_{i}$ and the target style $s_{j}$, the learning objective
function can be represented as:

$\mathcal{L}=\mathcal{L}_{rec}+\mathcal{L}_{cyc}+\mathcal{L}_{adv}+\mathcal{L}_{ret}+\mathcal{L}_{bow},$
(7)

where $\mathcal{L}_{rec}$, $\mathcal{L}_{cyc}$, $\mathcal{L}_{adv}$,
$\mathcal{L}_{ret}$ and $\mathcal{L}_{bow}$ denote the reconstruction loss,
the cycle reconstruction loss, the adversarial style loss, the retrieval loss,
and the bag-of-words loss, respectively.

#### Reconstruction Loss

According to previous works (Shen et al., 2017; Fu et al., 2018; John et al.,
2019), this loss is used to capture the informative features for
reconstructing itself:

$\mathcal{L}_{rec}=-\log P(x|x,s_{i}).$ (8)

#### Cycle Reconstruction Loss

Cycle consistency is usually included in the loss to improve the preservation
of content (Lample et al., 2019; Dai et al., 2019; Yi et al., 2020). For the
generated $\hat{y}$, the output of transferring back to the source style
should be consistent with $x$ as much as possible:

$\displaystyle\mathcal{L}_{cyc}=-\log
P(x|\hat{y},s_{i}),\;\;\hat{y}=G(x,s_{j}),$ (9)

where $G$ is our TSST model.

#### Adversarial Style Loss

If we only use the reconstruction and cycle construction losses, the model
merely learns to copy the input to the output. So we employ adversarial
training to build style supervision. Specifically, we utilize a classifier
with $M+1$ classes as the discriminator $C$, similar to Dai et al. (2019) and
Yi et al. (2020). The first $M$ classes represent the real texts in the
datasets, and the $(M+1)$-th class indicates generated fake texts. Since the
generated text $\hat{y}$ is expected to be classified as the target style
$s_{j}$, the adversarial style loss is defined as follows, and the negative
gradient of the discriminator is employed to update the model.

$\mathcal{L}_{adv}=-\log P_{C}(j|\hat{y}),$ (10)

As for the discriminator $C$, a loss function $\mathcal{L}_{C_{1}}$ is usually
used in previous works Dai et al. (2019) and Yi et al. (2020). In this paper,
we also ask the discriminator to identify the style of the retrieved samples.
Therefore, the loss function in our work can be written as:

$\displaystyle\mathcal{L}_{C}$
$\displaystyle=\mathcal{L}_{C1}+\mathcal{L}_{C2},$ (11)
$\displaystyle\mathcal{L}_{C1}$ $\displaystyle=-[\log P_{C}(i|x)+\log
P_{C}(i|G(x,s_{i}))$ $\displaystyle+\log P_{C}(M+1|\hat{y})],$
$\displaystyle\mathcal{L}_{C2}$ $\displaystyle=-[\log
P_{C}(j|Y^{x\rightarrow\hat{y}})+\log P_{C}(i|Y^{\hat{y}\rightarrow x})],$

where $Y^{x\rightarrow\hat{y}}$ and $Y^{\hat{y}\rightarrow x}$ denotes the
retrieved samples in the transfer process from $x$ to $\hat{y}$ and from
$\hat{y}$ to $x$, respectively.

To jointly learn the dense retriever with the other parameters and the target
style representation from the retrieved samples, we introduce two additional
losses to our objective.

#### Retrieval Loss

The retrieval loss is designed to capture the similarity between the style-
independent embeddings of the input sentence and the corresponding transferred
sentence:

$\mathcal{L}_{ret}=1-\cos(\bm{q}(x),\bm{q}(\hat{y})).$ (12)

#### Bag-of-Words Loss

This loss is proposed to encourage the generator to select some new words from
the retrieved sentences, making our model pay more attention to the retrieved
samples. In this way, the style representation in the target sentence will be
well adapted to the context. Let $\Omega$ denote a set of new words that
appear in the retrieved samples other than the input sentence $x$, and the
bag-of-words loss is defined as:

$\displaystyle\mathcal{L}_{bow}=\frac{1}{2}(\mathcal{L}_{bow}^{x\rightarrow\hat{y}}+\mathcal{L}_{bow}^{\hat{y}\rightarrow
x}),$ (13)
$\displaystyle\mathcal{L}_{bow}^{x\rightarrow\hat{y}}=\frac{1}{\|\Omega\|}\sum\nolimits_{i=1}^{\|\hat{y}\|}\sum\nolimits_{w\in\Omega}\log
p_{\hat{y}_{i}}(w),$ $\displaystyle\mathcal{L}_{bow}^{\hat{y}\rightarrow
x}=\frac{1}{\|\Omega^{\prime}\|}\sum\nolimits_{i=1}^{\|x\|}\sum\nolimits_{w\in\Omega^{\prime}}\log
p_{x_{i}}(w).$

Note that the Bag-of-Words loss is applied to both directions in the cycle
reconstruction process.

###  3.5 Discussion

Please note that there are also some other works to involve a retrieval module
to enhance the unsupervised style transfer, e.g. Li et al. (2018); Sudhakar et
al. (2019); Jin et al. (2019). The differences between our TSSM and these
works are listed as follows. 1) Our TSST model uses the retrieved samples to
directly control the style, instead of using them as an external knowledge or
pseudo-parallel corpus. 2) The retrieval module in our TSST can be trained in
an end-to-end way (together with the encoder and decoder), to improve the
style-independent text retrieval. 3) The retrieved samples influence the
decoder word by word. In this way, the information of the retrieved samples
will be fully exploited to learn a good context-aware style representation. It
may bring no benefit if we just use the retrieved samples without these
modifications, as shown in Sudhakar et al. (2019).

##  4 Experiment

In this section, we conduct experiments to study how well and why the proposed
TSST model alleviates the inconsistency problem. Furthermore, a detailed
ablation study is demonstrated to show each objective function’s contribution
to the overall performance.

###  4.1 Datasets

The experiments are conducted on two well-known transfer tasks, sentiment
transfer and formality transfer. The statistics of each datasets are shown in
Table 1.

Yelp Dataset111http://bit.ly/2LHMUsl. is widely used as the benchmark for
sentiment transfer. It is collected from restaurant and business reviews, with
each text marked as positive or negative. The same pre-processing as (Li et
al., 2018) is used in our experiment, and human references are also provided
for the test set.

GYAFC Dataset222https://github.com/raosudha89/GYAFC-corpus. denotes the
Grammarly’s Yahoo Answers Formality Corpus released by Rao and Tetreault
(2018), a typical benchmark for formality transfer. The GYAFC dataset contains
formal and informal sentences in two different domains, _Entertainment &
Music_ and _Family & Relationships_. In this paper, we use the latter one
because it is more popular in this area.

Dataset | Styles | Train | Dev | Test
---|---|---|---|---
Yelp | Positive | 266,041 | 2,000 | 500
Negative | 177,218 | 2,000 | 500
GYAFC | Formal | 51,967 | 2,247 | 1,019
Informal | 51,967 | 2,788 | 1,332

Table 1:  Statistics of Yelp and GYAFC datasets.

Model | Yelp |  | GYAFC
---|---|---|---
Acc$\uparrow$ | s-BLEU$\uparrow$ | r-BLEU$\uparrow$ | PPL$\downarrow$ | GM$\uparrow$ |  | Acc$\uparrow$ | s-BLEU$\uparrow$ | r-BLEU$\uparrow$ | PPL$\downarrow$ | GM$\uparrow$
CrossAlign (Shen et al., 2017) | 78.7 | 16.65 | 8.11 | 66 | 7.09 |  | 61.6 | 2.21 | 3.25 | 37 | 3.32
DRG (Li et al., 2018) | 88.1 | 36.75 | 16.66 | 100 | 10.4 |  | 58.2 | 31.57 | 21.88 | 103 | 9.65
MultiDec (Fu et al., 2018) | 45.4 | 40.07 | 15.07 | 188 | 8.51 |  | 24.5 | 16.08 | 11.95 | 151 | 5.54
B-GST (Sudhakar et al., 2019) | 82.4 | 30.82 | 16.32 | 156 | 9.52 |  | - | - | - | - | -
G-GST (Sudhakar et al., 2019) | 61.1 | 45.72 | 22.08 | 257 | 10.27 |  | - | - | - | - | -
DualRL (Luo et al., 2019) | 87.9 | 58.90 | 28.77 | 105 | 13.37 |  | 55.5 | 52.80 | 43.69 | 159 | 12.61
StyTrans (Dai et al., 2019) | 86.0 | 59.46 | 27.32 | 154 | 12.90 |  | 60.3 | 61.15 | 43.95 | 168 | 13.33
IMaT (Jin et al., 2019) | 93.9 | 16.92 | 11.26 | 14 | 9.08 |  | - | - | - | - | -
Revision (Liu et al., 2020) | 90.6 | 13.23 | 7.93 | 21 | 7.45 |  | 39.6 | 27.64 | 20.83 | 66 | 8.59
PFST (He et al., 2020) | 84.6 | 48.90 | 23.72 | 67 | 12.35 |  | 63.9 | 28.68 | 21.53 | 40 | 10.17
StyIns (Yi et al., 2020) | 90.9 | 53.10 | 26.09 | 110 | 12.80 |  | 69.9 | 61.87 | 47.80 | 140 | 14.32
TSST-random | 88.8 | 59.64 | 28.44 | 117 | 13.34 |  | 76.5 | 62.84 | 50.13 | 108 | 15.06
TSST-sparse | 90.7 | 59.03 | 28.71 | 108 | 13.46 |  | 75.3 | 64.03 | 50.39 | 101 | 15.15
TSST-dense | 91.8 | 59.34 | 28.89 | 108 | 13.54 |  | 74.1 | 63.70 | 50.49 | 103 | 15.06

Table 2:  Automatic evaluation results on Yelp and GYAFC dataset. Acc denotes
the accuracy of generated samples judged by the pre-trained classifier. s-BLEU
and r-BLEU stands for self-BLEU and ref-BLEU, respectively. Bold denotes the
best value in terms of each metric.

Model | Yelp |  | GYAFC
---|---|---|---
Sty | Cont | Flu | Cons |  | Sty | Cont | Flu | Cons
DualRL | 3.70 | 4.34 | 4.29 | 1.38 |  | 1.84 | 2.92 | 3.11 | 0.37
StyTrans | 2.98 | 4.18 | 3.54 | 1.04 |  | 2.43 | 3.27 | 3.53 | 0.49
PFST | 3.21 | 3.69 | 4.18 | 1.26 |  | 1.97 | 2.28 | 3.41 | 0.31
StyIns | 3.43 | 4.12 | 3.82 | 1.27 |  | 1.36 | 1.62 | 2.21 | 0.11
TSST-random | 3.64 | 4.34 | 3.84 | 0.99 |  | 2.84 | 3.55 | 3.59 | 0.51
TSST-sparse | 3.52 | 4.42 | 4.02 | 1.33 |  | 2.74 | 3.48 | 3.67 | 0.75
TSST-dense | 3.85 | 4.40 | 4.09 | 1.46 |  | 2.70 | 3.59 | 3.69 | 0.74

Table 3:  Human evaluation results on Yelp and GYAFC dataset. The
Krippendorff’s alpha of human rating on two datasets is 0.76 and 0.73,
respectively, indicating acceptable inter-annotator agreement.

###  4.2 Setups

Our baselines cover three different kinds of inductive learning approaches, as
described in Section 2. For the embedding based method, we choose CrossAlign
(Shen et al., 2017), StyTrans (Dai et al., 2019), PFST (He et al., 2020) and
StyIns (Yi et al., 2020). For the generator based method, MultiDecoder (Fu et
al., 2018) and DualRL (Luo et al., 2019) are selected for comparisons. Then,
Revision (Liu et al., 2020) is considered as the representative of the
discriminator based methods. At last, we also compare our model with previous
methods involved in retrieval, DRG (Li et al., 2018), IMaT (Jin et al., 2019),
B-GST and G-GST (Sudhakar et al., 2019). All baselines (including generated
results) are directly taken or implemented from their public source codes, so
the detailed settings are omitted in our paper.

For our proposed TSST model, we employ the LSTM as our encoder and decoder to
ensure the fairness of the experiment compared with previous methods.
Following Yi et al. (2020), we pre-train a forward LSTM language model in each
dataset and use its parameters to initialize our encoder and decoder. Similar
to Yi et al. (2020), the discriminator is a CNN-based classifier with Spectral
Normalization (Miyato et al., 2018), with the same word embeddings as the
encoder. The word embedding size, hidden state size, and the number of
retrieved samples $K$ are set to 256, 512, and 5, respectively. We exclude the
trivial candidates the same as the input sentence in the retriever. The
embeddings of all sentences for dense retrieval are updated every 200 steps.
To demonstrate the effectiveness of the sparse and dense retrievers, we
compare them with a random sampling retriever, and the corresponding TSST
model is denoted as TSST-random.

###  4.3 Evaluation Metrics

Previous works mainly focus on evaluating the style transfer methods from the
following three aspects, i.e. style transfer accuracy, content preservation,
and sentence fluency. Consequently, different automatic evaluation measures
such as accuracy, _self_ -BLEU, _ref_ -BLEU, and perplexity(PPL) are also used
in the evaluation. However, all of these metrics cannot well evaluate how well
a model alleviates the consistency problem, as we introduced before. So we
introduce an additional human evaluation in our experiment.

Automatic Evaluation To evaluate the style transfer accuracy, we first
finetune a pre-trained BERT-based (Devlin et al., 2019) classifier on each
dataset. The two classifiers achieve 98.6% and 89.9% accuracy on the test set
of Yelp and GYAFC, respectively. Then these classfiers are used to predict the
style label of the generated transferred sentences, and the classification
accuracy acts as the style transfer accuracy. Both _self_ -BLEU and _ref_
-BLEU are used for content preservation evaluations. The former is the BLEU
score between transferred sentences and source sentences, while the latter is
between transferred sentences and human references. Following Dai et al.
(2019) and Yi et al. (2020), we train a 5-gram language model KenLM (Heafield,
2011) for each style to measure the lanuguage fluency by the perplexity (PPL)
of transferred sentence. In addition, we report the geometric mean (GM) of
Acc, _self_ -BLEU, _ref_ -BLEU and $\frac{1}{\log\rm{PPL}}$ as the overall
performance.

Human Evaluation We recruit three annotators who have high-level language
skills for human evaluation. We choose four models with the highest GM scores
and three types of TSST models in this experiment. Following previous works
(Dai et al., 2019; Yi et al., 2020; Liu et al., 2020), we randomly selected
100 generated sentences (50 for each style) in the test set and the annotators
are required to score sentences from 1 to 5, in terms of each aspect, i.e.
style transfer accuracy (Sty), content preservation (Cont), and sentence
fluency (Flu), where 1 is the lowest and 5 is the highest. In addition, they
need to evaluate the consistency of the style words and other contexts in each
generated sentence. To make it more clear, consistency (Cons) focuses on
judging whether the modified parts are consistent with the retained content,
while the fluency only focuses on the grammatical errors. The consistency is
rated from 0 to 2, where 0, 1, and 2 stands for inconsistent, unsure, and
consistent, respectively.

###  4.4 Experimental Results

Model | Negative to Positive | Positive to Negative
---|---|---
input | it ’s just too expensive for what you get. | mustard beef ribs are a must.
DualRL | it ’s just fun for what you get. | mustard beef ribs are a mess.
StyTrans | it ’s just too expensive for what you get. | mustard beef ribs are a negative.
StyIns | it ’s just very fun for what you get. | mustard beef ribs were a total.
PFST | it ’s just right for what you get. | gross.
Revision | it ’s too expensive for what you get and it ’s always good. | the beef ribs are a must do not have a bbq beef.
TSST-dense | it ’s just really reasonable for what you get. | mustard beef ribs are a joke.
Retrieved Samples |  $u_{1}$ \- the price is reasonable for what you get. |  $u_{1}$ \- garlic mashed potatoes were a joke.
$u_{2}$ \- prices are very reasonable for what you get. |  $u_{2}$ \- all olive gardens are a joke.
$u_{3}$ \- prices are reasonable for the qualiy of what you get. |  $u_{3}$ \- the ribs are over cooked.
$u_{4}$ \- good food , a little expensive for what you get. |  $u_{4}$ \- our food was barely edible.
$u_{5}$ \- definitely a good price for what you get. |  $u_{5}$ \- cold grits are n’t a treat.

Table 4: Case Study. Red denotes _positive_ expressions, blue denotes
_negative_ expressions, and bold denotes the expression taken from retrieved
samples.

Automatic Evaluation As listed in Table 2, we can see that our transductive
learning models significantly improve the overall performance on both
datasets, as compared with the inductive learning baselines. As for the four
specific evaluation measures, our model achieves better results in terms of
three of them, i.e. Acc for style transfer accuracy, s-BLEU, and r-BLEU for
content preservation, and all our models are able to generate sentences with
relatively low perplexity. Though previous models achieve the best on a single
metric, a significant drawback can be found on another metric. For the TSST-
random, although target-style relevant words in the random samples are not
consistent with the input content, the few modifications and the use of
target-style strong relevant words will still make the random baseline achieve
the high automatic metrics. Thus we need human evaluation.

Human Evaluation Results are shown in Table 3. Firstly, the comparison results
are consistent with the automatic evaluation results in terms of both style
accuracy, content preservation, and fluency, indicating the reliability of our
human evaluation. More importantly, our TSST-sparse and TSST-dense models
achieve the highest consistency score, as compared to other baselines, showing
the superiority of our transductive learning approach in tackling the
inconsistency problem. In contrast, the consistency will become worse if the
retrieved samples are not related to the input, as shown in TSST-random, which
further demonstrate the soundness of our approach. Comparing TSST-sparse and
TSST-dense, we can see that joint learning retriever yields better consistency
results, which is accordant with previous studies.

Case Study To better understand what transductive learning bring to text style
transfer task, Table 4 shows some transferred examples from the Yelp test set.
For the first example to transfer from negative to positive, DualRL and StyIns
are able to capture the style transfer but with inappropriate expressions,
e.g. ‘ _fun_ ’ or ‘ _very fun_ ’. StyTrans and Revision fail to do the style
transfer, either just copy or use negative expressions. Our TSST-dense model
produces perfect results by using ‘reasonable’ as the transferred style
expression, learned from the retrieved examples, as shown in the table.
Similar results are observed for the second example to transfer positive to
negative. More importantly, although there is no retrieved examples containing
exactly the same phrase ‘ _mustard beef ribs_ ’, our model still capture the
negative pattern like ‘ _[food] is a joke_ ’ to complete the style transfer
process.

Model | Acc$\uparrow$ | s-BLEU$\uparrow$ | r-BLEU$\uparrow$ | PPL$\downarrow$ | GM$\uparrow$
---|---|---|---|---|---
TSST-dense | 91.8 | 59.34 | 28.89 | 108 | 13.54
$-$ $\mathcal{L}_{rec}$ | 52.7 | 0.40 | 0.37 | N/A | 1.01
$-$ $\mathcal{L}_{adv}$ | 81.3 | 18.96 | 8.48 | N/A | 6.67
$-$ $\mathcal{L}_{cyc}$ | 89.0 | 52.07 | 24.71 | 134 | 12.37
$-$ $\mathcal{L}_{ret}$ | 89.6 | 59.36 | 28.65 | 110 | 13.42
$-$ $\mathcal{L}_{bow}$ | 89.3 | 55.26 | 26.43 | 121 | 12.84
$-$ $\mathcal{L}_{C1}$ | 95.6 | 7.6 | 3.9 | N/A | 4.47
$-$ $\mathcal{L}_{C2}$ | 87.4 | 61.41 | 28.58 | 112 | 13.43

Table 5: Ablation study on the Yelp dataset, where ‘$-$’ means removing one of
the loss terms in the objective functions, and N/A is a very large value.

Ablation Study To study the role of each loss function in the objective
function, we remove them one at a time and train the model from scratch. Due
to the high cost of the human evaluation, we only report the automatic results
on the Yelp dataset, as shown in Table 5. We can see that each loss
contributes to the performance of the model, and their combination performs
the best.

Figure 3:  The influence of the number of retrieved samples $K$ on the Yelp
dataset.

We also conduct experiments to explore the influence of different numbers of
retrieved samples, as shown in Figure 3. Specifically, we test four different
values, i.e. $K=1,3,5,10$. We can see that the overall performance of the
model gradually increases with the increase of $K$, and become stable for 3,
5, and 10. For the sake of both effectiveness and efficiency, we set $K=5$ in
our experiments.

##  5 Conclusions and Future Work

Previous style transfer models are mainly based on inductive learning
approach, thus suffer from the inconsistent style expression problem with lack
of the parallel corpus as supervisions. To tackle this problem, we propose a
novel transductive learning approach for unsupervised text style transfer. The
key idea of our TSST model is to learn context-aware style expressions via
retrieved samples from the target style datasets. Experimental results on two
typical style transfer tasks show that TSST significantly improves the
performances in terms of both automatic and human evaluation.

Our proposed transductive learning approach is very general, and this work
mainly focus on the embedding-based methods. In future, we plan to extend our
approach to other methods, such as decoder-based and discriminator-based
methods. Moreover, we will try more powerful retrieval methods, such as DPR
(Karpukhin et al., 2020).

## Ethical Considerations

We honor and support the ACL code of Ethics. The paper focuses on style
transfer, which aims to change the style of the text while preserving the
semantic content. We recognize the style transfer methods may be misused to
generate misinformation, e.g. fake customer reviews. However, the style
transfer methods can also provide strong support for mitigating harmful biases
in online information, e.g. the transfer from offensive to non-offensive
(Nogueira dos Santos et al., 2018; Tran et al., 2020) and from biased to
neutral (Pryzant et al., 2020). Overall, it is still meaningful to continue
research into this work on the basis of predecessors. Simultaneously, the
datasets we used in this paper are all from previously published works and do
not involve privacy or ethical issues.

## Acknowledgements

This work was supported by National Natural Science Foundation of China (NSFC)
under Grants No. 61906180, No. 61773362 and No. 91746301, National Key R&D
Program of China under Grants 2020AAA0105200 and the Tencent AI Lab Rhino-Bird
Focused Research Program (No. JR202033).

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