## 1 Introduction  
With the rapid development of the Chinese economy and continuous improvement in China's comprehensive national strength, Chinese is gaining increasing prominence as an international language. Consequently, increasing amounts of foreign scholars are actively participating in the study of the Chinese language. At present, 10 UN-affiliated agencies have adopted Chinese as the official language, 76 countries have incorporated Chinese into their national education systems, and nearly 200 million people have learned and used Chinese abroad.  
The \"colloquialization\" trend in written English among Chinese students has drawn attention in the field of foreign language teaching. Scholars, such as [PERSON] et al. (2003) [1], [PERSON] (2012) [2], and [PERSON] (2013) [3], have investigated this trend using various features. However, overcoming this trend poses a significant challenge in second language education. To address this challenge, a de-colloquialization system can assist Chinese language learners in improving their understanding of Chinese expression, leading to an increasing demand for the use of de-colloquialization in the Chinese language.  
De-colloquialization in the Chinese language greatly benefits fields that require extensive text writing in daily life. People may unconsciously include colloquial sentences lacking proper grammatical or spelling errors when writing formal texts due to their habits regarding using colloquial expressions in communication. Furthermore, manual proofreading is time-consuming and costly. In education, de-colloquialization can improve the language standards and the rigour of textbooks. In the business sector, de-colloquialization can enhance the formality of contracts, showing respect for the cooperating parties. In journalism, de-colloquialization technology can optimize news releases to improve media professionalism and credibility by avoiding colloquial expressions.  
The concept of machine translation has been applied to other tasks, as evidenced by the work of [PERSON] and [PERSON] (2017), who proposed a grammar correction model based on statistical machine translation, which greatly improved text correction accuracy [4]. One year later, [PERSON] and [PERSON] (2018) proposed a correction model using a multilayered convolutional encode-decode neural network. Due to the architecture design of seq2 seq, their model can not only correct grammatical errors but also correct spelling and word priors in the text [5]. This article considers de-colloquialization a translation task, using neural machine translation to \"translate\" colloquial expressions in text into written expressions. However, in China, there is relatively little research on the de-colloquialization of Chinese text, and no parallel corpus datasets are available to train de-colloquialization tasks. To address this issue, we created a parallel corpus dataset using the back-translation method proposed in 2015 by Facebook [6]. The experiment verifies the effectiveness of the dataset and investigates the performance of two mainstream neural machine translation models: the Sequence to Sequence (Seq2 Seq) model (2014) [7] and the Transformer model (2017) [8]. The experiment demonstrates that these models can handle de-colloquialization tasks and produce satisfactory results in human evaluation.  
The purpose of this paper is to explore whether the end-to-end translation approach of neural machine translation can be applied to the task of expression style conversion. Furthermore, the goal is to identify the most suitable framework for this task, which can be used for future optimization research. The technique proposed in this paper fills the gap in the study of expression style conversion in natural language processing and lays a foundation for further research in this area.