Formality style transfer is the task of converting informal sentences to
grammatically-correct formal sentences, which can be used to improve
performance of many downstream NLP tasks. In this work, we propose a
semi-supervised formality style transfer model that utilizes a language
model-based discriminator to maximize the likelihood of the output sentence
being formal, which allows us to use maximization of token-level conditional
probabilities for training. We further propose to maximize mutual information
between source and target styles as our training objective instead of
maximizing the regular likelihood that often leads to repetitive and trivial
generated responses. Experiments showed that our model outperformed previous
state-of-the-art baselines significantly in terms of both automated metrics and
human judgement. We further generalized our model to unsupervised text style
transfer task, and achieved significant improvements on two benchmark sentiment
style transfer datasets.

本文提出了一种使用语言模型鉴别器的半监督式正式语言风格转换模型，通过最大化生成句子为正式语言的可能性进行训练，并提出了最大化样式源和目标之间互信息作为训练目标。实验结果表明，在自动化指标和人类判断方面，我们的模型显著优于之前的最新基线，并将其推广到无监督文本风格转换任务中，在两个情感风格转换基准数据集上均获得了显着进展。