PGST: a Polyglot Gender Style Transfer method

TL;DR

PGST introduces a novel multilingual gender style transfer method that effectively manipulates gendered text across English and Persian, outperforming existing gender identification models in fluency and content preservation.

Contribution

It is the first to propose a polyglot gender style transfer approach applicable to multiple languages using a combination of pre-trained embeddings, character classifiers, and beam search.

Findings

Successfully applied to English and Persian corpora.

Reduced gender identification accuracy by 45.6% and 39.2%.

Achieved competitive results compared to state-of-the-art methods.

Abstract

Recent developments in Text Style Transfer have led this field to be more highlighted than ever. The task of transferring an input's style to another is accompanied by plenty of challenges (e.g., fluency and content preservation) that need to be taken care of. In this research, we introduce PGST, a novel polyglot text style transfer approach in the gender domain, composed of different constitutive elements. In contrast to prior studies, it is feasible to apply a style transfer method in multiple languages by fulfilling our method's predefined elements. We have proceeded with a pre-trained word embedding for token replacement purposes, a character-based token classifier for gender exchange purposes, and a beam search algorithm for extracting the most fluent combination. 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 ended up defeating our proposed gender identification model by 45.6% and 39.2%, respectively. While this research's focus is not limited to a specific language, our obtained evaluation results are highly competitive in an analogy among English state of the art methods.