Advances of Informal to Formal Persian Text Conversion: A Survey

The Formality Style Transformation (FST) task aims to convert informal text into formal while preserving the original meaning. It is essential to improve the performance of a variety of NLP tasks that rely on formal language input. Nevertheless, it remains challenging for low-resource languages due to the need for high-quality parallel corpora. On the other hand, languages that use non-Latin scripts, such as Persian (Farsi), are morphologically complex and underrepresented, which limits the performance of pretrained models. More specifically, informal Persian text often contains colloquial expressions, dialectal variations, non-standard spellings, and code-switching with English or Arabic words, which makes it difficult for models to generate accurate formal equivalents. In this survey, we provide a systematic overview of existing methods, datasets, and evaluation metrics for Persian formality style transformation. Finally, we discuss open challenges and identify future research directions in this domain.