  In most cases, the lack of parallel corpora makes it impossible to directly
train supervised models for the text style transfer task. In this paper, we
explore training algorithms that instead optimize reward functions that
explicitly consider different aspects of the style-transferred outputs. In
particular, we leverage semantic similarity metrics originally used for
fine-tuning neural machine translation models to explicitly assess the
preservation of content between system outputs and input texts. We also
investigate the potential weaknesses of the existing automatic metrics and
propose efficient strategies of using these metrics for training. The
experimental results show that our model provides significant gains in both
automatic and human evaluation over strong baselines, indicating the
effectiveness of our proposed methods and training strategies.
