Defeating Author Gender Identification with Text Style Transfer

Text Style Transfer can be named as one of the most important Natural Language Processing tasks. Up until now, there have been several approaches and methods experimented for this purpose. In this work, we introduce PGST, a novel polyglot text style transfer approach in gender domain composed of different building blocks. If they become fulfilled with required elements, our method can be applied in multiple languages. We have proceeded with a pre-trained word embedding for token replacement purposes, a character-based token classifier for gender exchange purposes, and the beam search algorithm for extracting the most fluent combination among all suggestions. Since different approaches are introduced in our research, we determine a trade-off value for evaluating different models' success in faking our gender identification model with transferred text. To demonstrate our method's multilingual applicability, we applied our method on both English and Persian corpora and finally ended up defeating our proposed gender identification model by 45.6% and 39.2%, respectively, and obtained highly competitive evaluation results in an analogy among English state of the art methods.