Language style transferring rephrases text with specific stylistic attributes
while preserving the original attribute-independent content. One main challenge
in learning a style transfer system is a lack of parallel data where the source
sentence is in one style and the target sentence in another style. With this
constraint, in this paper, we adapt unsupervised machine translation methods
for the task of automatic style transfer. We first take advantage of
style-preference information and word embedding similarity to produce
pseudo-parallel data with a statistical machine translation (SMT) framework.
Then the iterative back-translation approach is employed to jointly train two
neural machine translation (NMT) based transfer systems. To control the noise
generated during joint training, a style classifier is introduced to guarantee
the accuracy of style transfer and penalize bad candidates in the generated
pseudo data. Experiments on benchmark datasets show that our proposed method
outperforms previous state-of-the-art models in terms of both accuracy of style
transfer and quality of input-output correspondence.

本文提出一种使用无监督机器翻译方法来实现自动语言风格转换的方法，利用样式偏好信息和单词嵌入相似性来生成伪平行数据，并采用迭代回译方法来联合训练两个神经机器翻译（NMT）系统，其中引入样式分类器来控制噪声。实验表明，该方法在语言风格转换的精确度和输入输出对应质量方面优于以前的现有模型。

风格转换作为无监督机器翻译

Style Transfer as Unsupervised Machine Translation

Language style transfer is the problem of migrating the content of a source
sentence to a target style. In many of its applications, parallel training data
are not available and source sentences to be transferred may have arbitrary and
unknown styles. First, each sentence is encoded into its content and style
latent representations. Then, by recombining the content with the target style,
we decode a sentence aligned in the target domain. To adequately constrain the
encoding and decoding functions, we couple them with two loss functions. The
first is a style discrepancy loss, enforcing that the style representation
accurately encodes the style information guided by the discrepancy between the
sentence style and the target style. The second is a cycle consistency loss,
which ensures that the transferred sentence should preserve the content of the
original sentence disentangled from its style. We validate the effectiveness of
our model in three tasks: sentiment modification of restaurant reviews, dialog
response revision with a romantic style, and sentence rewriting with a
Shakespearean style.

该研究论文介绍了一种利用内容和风格潜在表示进行语言风格转换的方法，并证明了该模型在餐厅评论情感修改、浪漫风格对话回复和莎士比亚风格语句重写等三种任务中具有有效性。

从具有任意未知样式的句子进行语言风格转移

Language Style Transfer from Sentences with Arbitrary Unknown Styles

Expressing in language is subjective. Everyone has a different style of
reading and writing, apparently it all boil downs to the way their mind
understands things (in a specific format). Language style transfer is a way to
preserve the meaning of a text and change the way it is expressed. Progress in
language style transfer is lagged behind other domains, such as computer
vision, mainly because of the lack of parallel data, use cases, and reliable
evaluation metrics. In response to the challenge of lacking parallel data, we
explore learning style transfer from non-parallel data. We propose a model
combining seq2seq, autoencoders, and adversarial loss to achieve this goal. The
key idea behind the proposed models is to learn separate content
representations and style representations using adversarial networks.
Considering the problem of evaluating style transfer tasks, we frame the
problem as sentiment transfer and evaluation using a sentiment classifier to
calculate how many sentiments was the model able to transfer. We report our
results on several kinds of models.

使用 seq2seq，自编码器和对抗性损失模型进行非平行数据样本的语言风格转移。通过使用情感分类器评估样式转移任务，报告了模型在几种模型上的结果。