  Text style transfer has gained increasing attention from the research
community over the recent years. However, the proposed approaches vary in many
ways, which makes it hard to assess the individual contribution of the model
components. In style transfer, the most important component is the optimization
technique used to guide the learning in the absence of parallel training data.
In this work we empirically compare the dominant optimization paradigms which
provide supervision signals during training: backtranslation, adversarial
training and reinforcement learning. We find that backtranslation has
model-specific limitations, which inhibits training style transfer models.
Reinforcement learning shows the best performance gains, while adversarial
training, despite its popularity, does not offer an advantage over the latter
alternative. In this work we also experiment with Minimum Risk Training, a
popular technique in the machine translation community, which, to our
knowledge, has not been empirically evaluated in the task of style transfer. We
fill this research gap and empirically show its efficacy.
