Empirical Evaluation of Supervision Signals for Style Transfer Models
This work addresses the challenge of assessing model components in style transfer for researchers, though it is incremental in evaluating existing techniques.
The paper tackled the problem of evaluating supervision signals for text style transfer models by empirically comparing backtranslation, adversarial training, and reinforcement learning, finding that reinforcement learning performed best with concrete gains, while also introducing Minimum Risk Training as a novel effective method.
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.