  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.
