## 4 Results
### Result and Analysis  
This article employs two outstanding models in the field of machine translation for training to investigate whether the principles of machine translation can be applied to the task of de-colloquialization. It is observed that the Seq2 Seq+attention model failed to fit the dataset for de-colloquialization well. Even after 100 epochs of training, the loss value remained as high as 5.77. To verify whether this phenomenon is attributed to the large size of the dataset, this article resorts to 1 million parallel corpora to further train the model. However, even after 100 epochs, the loss value reached 5.15, as shown in Figure 4, indicating a limited improvement in the loss value. In contrast, the Transformer model converges to remarkable effectiveness within only 60 epochs, as shown in Figure 5.  
The comparison of experimental results is presented in Table 6.  
Figure 4: Seq2 Seq+attention training process. It can be observed that after 20 epochs, the loss value starts to descend at a very slow pace. Even though the model is able to continue converging after 100 epochs, its convergence efficiency is considerably reduced. Figure 5: Transformer training process. The process of how the loss values of the Transformer model on both the training and validation sets decrease is shown in (**a**), while the perplexity score of the Transformer model is shown in (**b**). After being trained for 60 epochs, the model’s loss value is as low as 1.49, and the validation loss value also decreases to 1.42. Furthermore, the perplexity score of the model experiences a significant drop. Table 6 displays detailed metrics for the training of both the Seq2 Seq+attention and Transformer models. These metrics include the amount of data used for training, the final converged loss value, the character-level and word-level BLEU scores (a character-matching algorithm compared to the reference answers), the human evaluation score, and the training time. The initial line of data aims to showcase the computation of BLEU scores for a test set comprising 49,000 paired spoken and written texts. These scores are considered fundamental indicators capable of reflecting the similarity between spoken and written texts. They are utilized for comparison with the BLEU scores of sentences that have undergone model transformation. It is observed that, for sentences transformed by the Transformer model, whether at the character-level BLEU or word-level BLEU, all exceed the baseline indicator. This indicates that the sentences transformed by the model are more closely aligned with written language. Seq2 seq is a pure translation model without an attention mechanism. In the case of the Transformer (base) model, it has four attention heads in the multi-head attention mechanism and a hidden layer dimension of 256. On the other hand, the Transformer model has eight attention heads in the multi-head attention mechanism and a hidden layer dimension of 512. As the Seq2 Seq+attention model did not converge to an ideal state, it was excluded from the model evaluation. It is notable that the Transformer model outperforms the Seq2 Seq+attention model in terms of convergence speed and training duration. Regarding the task of de-colloquialization, since both the target and source languages are Chinese, the difference between the grammar and semantics of the two languages is relatively small. At this point, the multi-head attention mechanism can effectively consolidate features at various dimensions, while Seq2 Seq+attention may struggle to learn useful features in a single dimension and could forget some relevant features. Therefore, the Seq2 Seq+attention model may not perform well in converging within linguistic datasets with a smaller degree of language distance. The reason why the calculated BLEU score is very high is that the source language and the target language are already very similar.