## 3 Materials and Methods
### Data Construction Based on Back-Translation  
Back-translation is a technique proposed by [PERSON] et al. in 2015 to improve the performance of neural network translation models. This technique involves using monolingual corpora to generate synthetic parallel corpora that can be used to enhance the model's performance. Although formal written Chinese can be easily found in publicly available corpora, it is difficult to find colloquialism that is semantically aligned with the written language. This is why our proposed method uses back-translation to construct a parallel corpus for de-colloquialization. Afterwards, [PERSON] et al. (2018) proposed back-translation as a dedicated data-augmentation technique to optimize the performance of question-answering models ([PERSON] et al., 2018). Although the approach to back-translation may differ slightly depending on the specific application, the overall idea remains the same. The back-translation method for data augmentation translated language 1 into language 2 and then translated language 2 back into language 1. Multiple intermediate languages can also be used. However, to ensure that the semantics of the original language are not altered, it is crucial to have a high-quality translation model that can accurately preserve the semantic content of the text during translation. The effect of back-translation data augmentation using the Google Translate engine is shown in Table 2.  
Figure 1: Overall flowchart. The collected written text is translated back to colloquial language through back-translation to obtain colloquialized texts that are semantically equivalent. After review, they are compiled into a parallel corpus dataset, from which a portion is extracted as the test set. Then, training is conducted separately on Seq2 Seq+attention and Transformer models to obtain two text de-colloquialization models. Subsequently, the test set is predicted using each model, and finally, the model performance is evaluated. It can be observed that the back-translated sentences are somewhat different from the original sentences, but their meaning is basically the same. The back-translation method used in this paper mainly focuses on sentence corpora, that is, how to generate colloquialisms from written language using back-translation. According to the research of [PERSON], [PERSON], [PERSON], and others, regardless of whether Chinese learners' mother tongue is English or Thai, they tend to use colloquialisms when writing in Chinese. The nature of neural networks is a biomimetic structure that simulates the neural information transmission inside the human brain. Language is also a kind of information, and some training methods of neural networks are very similar to the way humans learn language. For example, when bidirectional encoder representations from transformers (BERT) train word vectors, they randomly mask a part of the training data, which is very similar to the fill-in-the-blank questions that humans practice when learning English [17]. Therefore, does the language model trained by neural networks also have the characteristic of human colloquialism when generating sentences? We designed experiments to select two mature neural network translation models on the market - A Translator and B Translator. The reason for choosing mature translation models is to ensure that the semantics of the sentences will not change after back-translation and to save the cost of training translation models. The reason for selecting two different translation models is that when the same translation model is used to train translations in different directions between two languages, the training corpus dataset used is the same, which may cause the back-translated sentences to be similar to the original sentences. To solve this problem, multiple intermediate languages can be selected, as shown in Table 1. Although the original text and the final translation in Table 1 are different, the degree of colloquialism is not very high because Google Translate's performance is good enough.