## 5 Discussion  
This article utilizes the back-translation approach to construct a parallel corpus dataset of written language and colloquialisms for training purposes. Regarding the question of why written language, after undergoing back-translation, can produce sentences with colloquial characteristics, the following discussion is provided:  
* In this article, the same translation model is not used for back-translation. Instead, two translation models, A and B, are selected for the back-translation process, with translation model B being inferior to translation model A. This is performed to simulate the scenario where humans perform translations. The two language models represent individuals with different native languages and translation proficiency levels. When written language is translated into an intermediary language using translation model A, we aim for the intermediary language to better preserve the original intent of the written language. Therefore, translation model A, known for its better translation performance, is used for this step. It simulates a translator with a higher proficiency level who tends to use advanced vocabulary and expressions. Next, the intermediary language is translated back into the source language using the second translation model, B. In this step, we intentionally introduce noise to create colloquial features. Therefore, translation model B, known for its poorer translation performance, is selected. This step simulates a translator with a lower proficiency level who avoids using advanced vocabulary and expressions but aims for a more colloquial and informal style. Consequently, in this article, we employ this method to simulate the translation process performed by humans and generate noise that introduces colloquial features. Since both translation models are well trained, there is no concern about significant changes in the semantic meaning of the translated sentences. * When selecting an intermediary language, it is advisable to choose a low-resource language that is significantly different from the source language in terms of grammar and lexical representation. This is because the greater the distance between the intermediary language and the source language, the more challenging the translation becomes, resulting in a higher level of noise that introduces colloquial features. Additionally, selecting a low-resource language is preferred because there is currently limited training data available for such languages. As a result, the performance of the translation model is further weakened, allowing for the generation of more noise during the back-translation process. In terms of model training, this paper selects two different machine translation models, Seq2 Seq+attention and Transformer, and compares their fitting performance to determine the optimal model for subsequent work. It was found that when training on a dataset of 5 million parallel sentence pairs, Seq2 Seq+attention was unable to fit the data, while  
\begin{table}
\begin{tabular}{c c c} \hline
**Language** & **Original Sentence** & **De-Colloquialization** \\ \hline Chinese & \",  
Transformer achieved good results after only 60 batches. To investigate whether the inability to fit the data was due to the large dataset, Seq2 Seq+attention was trained on a smaller dataset of 1 million parallel sentence pairs, but the results still showed a lack of fitting. This may be because Seq2 Seq+attention was designed for different source and target languages with significant feature differences, enabling it to effectively capture those features. However, in the task of text de-colloquialization, both the source and target languages are the same, making it difficult for Seq2 Seq+attention to effectively capture colloquial features. The disparity may be attributed to the introduction of the multi-head attention mechanism in the Transformer model, which enables effective feature extraction across different linear spaces and various dimensions.