This study explores the formality style transfer in Persian, particularly
relevant in the face of the increasing prevalence of informal language on
digital platforms, which poses challenges for existing Natural Language
Processing (NLP) tools. The aim is to transform informal text into formal while
retaining the original meaning, addressing both lexical and syntactic
differences. We introduce a novel model, Fa-BERT2BERT, based on the Fa-BERT
architecture, incorporating consistency learning and gradient-based dynamic
weighting. This approach improves the model's understanding of syntactic
variations, balancing loss components effectively during training. Our
evaluation of Fa-BERT2BERT against existing methods employs new metrics
designed to accurately measure syntactic and stylistic changes. Results
demonstrate our model's superior performance over traditional techniques across
various metrics, including BLEU, BERT score, Rouge-l, and proposed metrics
underscoring its ability to adeptly navigate the complexities of Persian
language style transfer. This study significantly contributes to Persian
language processing by enhancing the accuracy and functionality of NLP models
and thereby supports the development of more efficient and reliable NLP
applications, capable of handling language style transformation effectively,
thereby streamlining content moderation, enhancing data mining results, and
facilitating cross-cultural communication.

研究探索了波斯语的形式风格转换，旨在将非正式的文本转化为正式的文本，保持原始含义，并改善了现有自然语言处理工具在数字平台上普遍存在的非正式语言问题。通过引入一种新的模型 Fa-BERT2BERT，基于 Fa-BERT 架构，结合一致性学习和基于梯度的动态权重，增强模型对语法变化的理解，平衡损失部分，并通过新的度量标准对该模型与传统方法进行了评估，结果表明其在多个指标上的卓越性能，包括 BLEU、BERT 分数、Rouge-l 和突出表现了其在波斯语风格转换复杂性方面的能力。该研究通过提高 NLP 模型的准确性和功能性，显著贡献于波斯语的处理并支持更高效可靠的 NLP 应用程序的发展，能够有效处理语言风格转换，从而简化内容的管理，增强数据挖掘结果，促进跨文化交流。