  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.
