Text style transfer rephrases a text from a source style (e.g., informal) to
a target style (e.g., formal) while keeping its original meaning. Despite the
success existing works have achieved using a parallel corpus for the two
styles, transferring text style has proven significantly more challenging when
there is no parallel training corpus. In this paper, we address this challenge
by using a reinforcement-learning-based generator-evaluator architecture. Our
generator employs an attention-based encoder-decoder to transfer a sentence
from the source style to the target style. Our evaluator is an adversarially
trained style discriminator with semantic and syntactic constraints that score
the generated sentence for style, meaning preservation, and fluency.
Experimental results on two different style transfer tasks (sentiment transfer
and formality transfer) show that our model outperforms state-of-the-art
approaches. Furthermore, we perform a manual evaluation that demonstrates the
effectiveness of the proposed method using subjective metrics of generated text
quality.

本研究利用强化学习生成评估器结构，使用基于注意力的编码 - 解码器转换源风格的句子至目标风格，并进行了语义和句法限制的对抗训练风格鉴别器，成功实现了无平行训练语料库的文本风格转移，并在两种不同的风格转移任务（情感转移和形式转移）中取得了优于现有方法的结果，并进一步进行了手动评估，证明了所提出方法在生成文本质量方面的有效性。