Empirical Evaluation of Supervision Signals for Style Transfer Models

TL;DR

This paper empirically compares different supervision signals for style transfer models, revealing reinforcement learning as the most effective, while also evaluating the novel application of Minimum Risk Training in this context.

Contribution

It provides the first empirical evaluation of Minimum Risk Training for style transfer and compares major optimization paradigms, highlighting reinforcement learning's superior performance.

Findings

Reinforcement learning outperforms backtranslation and adversarial training.

Backtranslation has model-specific limitations hindering style transfer.

Minimum Risk Training is effective for style transfer, as empirically demonstrated.

Abstract

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.