Formality transfer is a subfield of text style transfer that focuses on altering the level of formality in a given text while preserving its original meaning. It has recently gained significant attention due to its practical applications in various natural language processing (NLP) tasks. Whether it is for refining customer support dialogues, enhancing automated translation systems, tailoring educational content, or moderating online user-generated content, the ability to modify the formality of a sentence while retaining its core message is increasingly critical. The challenge is not just in generating fluent outputs, but in ensuring that the nuanced shifts in tone—formal to informal or vice versa—are both contextually appropriate and semantically accurate.
This study offers a deep and structured overview of formality transfer as applied to Arabic text, a relatively underexplored area in NLP compared to its English-language counterpart. While the task of formality transfer has been studied extensively in English and some other major languages, the research community is only beginning to delve into how it functions in languages like Arabic—languages that feature not only rich morphology and syntactic complexity but also a broad spectrum of formality levels that reflect deep sociolinguistic structures.
To provide meaningful insights into this emerging research area, we conducted an exhaustive literature review of academic studies focused on formality transfer, specifically from July 2010 to April 2024. Our aim was not only to compile existing efforts but also to synthesize them in a way that highlights the development trajectory of the field, the methodologies being used, and the challenges researchers have faced when dealing with Arabic.
Our approach views formality transfer in Arabic through a lens similar to machine translation. Just as machine translation maps a sentence from one language to another, formality transfer can be framed as mapping a sentence from one register or style (e.g., colloquial, informal) to another (e.g., Modern Standard Arabic, formal). This conceptualization opens up a space to borrow from techniques and architectures widely used in neural machine translation (NMT), such as sequence-to-sequence models, transformer-based architectures, and encoder-decoder frameworks. It also allows us to explore the utility of parallel corpora—texts aligned by meaning but differing in style or formality level—as a basis for training and evaluation.
This perspective is particularly relevant in the context of Arabic, where the distinction between Dialect Arabic (used in everyday conversation) and Modern Standard Arabic (MSA) (used in formal writing, education, and media) is pronounced. In some ways, converting from a dialect to MSA mirrors the process of translating between two languages. Therefore, techniques developed for low-resource machine translation tasks could be beneficially adapted for Arabic formality transfer, especially when parallel data is scarce or nonexistent.
One of the key contributions of this work is in identifying the linguistic and resource-based challenges that make formality transfer in Arabic uniquely difficult compared to other languages. Arabic is characterized by a rich morphological structure, high inflectional variability, and diglossia—the existence of two (or more) levels of the language used in different social contexts. These features mean that formality is not just reflected in vocabulary choices but also in verb forms, sentence structures, and even punctuation.
Furthermore, Arabic lacks the large-scale annotated datasets that are typically used to train and evaluate machine learning models. While there are several corpora available for English and Chinese formality transfer, for Arabic, datasets remain limited in both quantity and coverage. This scarcity significantly hampers the development and benchmarking of models in this space. Additionally, the criteria for defining “formal” and “informal” Arabic can vary, further complicating both annotation and evaluation processes.
Despite these advancements, our study uncovers several research gaps that present opportunities for future work. First, there is a need for standardized benchmarks—including shared tasks and evaluation protocols—that would enable fair comparison of models across studies. Second, the development of large-scale, high-quality parallel corpora aligned by formality level is crucial for meaningful progress. Third, interdisciplinary collaborations with sociolinguists could provide richer insights into how formality functions in different Arabic-speaking contexts and inform more culturally grounded models.
Moreover, user-centered applications of formality transfer are still rare in Arabic. Imagine educational platforms that can simplify or formalize texts for different learner levels, or chatbots that can adjust their register based on user preferences or cultural norms. These scenarios underscore the practical value of advancing this field.
Finally, ethical considerations must not be overlooked. Automatically transforming the formality of text could introduce or remove nuances that have social implications, especially in sensitive domains like legal or medical communication. Transparency in how models make such decisions, and ensuring that outputs align with user intent, will be important going forward.
In conclusion, this study underscores the growing importance of formality transfer in Arabic NLP, a field that is still in its early stages but rich with potential. By framing formality transfer as a translation-like task, identifying the unique challenges posed by the Arabic language, and reviewing the methodological trends in recent literature, we aim to set a foundation for further research and innovation.
There is a clear need for more targeted tools, datasets, and evaluation frameworks designed specifically with Arabic’s linguistic characteristics in mind. We encourage researchers, linguists, and developers to collaborate across disciplines to push the boundaries of what’s possible in Arabic formality transfer.
Whether in service of improving educational tools, enhancing multilingual communication, or simply making online content more accessible and respectful, formality transfer holds promise as a powerful component of the next generation of NLP applications.