## 4 Results and Discussion
### RQ3: Accuracy of the Translation Style  
Once the translations have been assessed individually and the reliability of the reference translation has been confirmed, the next objective is to identify which translator produces translations that most closely align with the desired style.  
The \(p\)-values obtained from the pairwise comparisons for paired samples are presented in Table 11.  
The \(p\)-values from the Pearson correlation test that uses the INCIBE translations as a reference are \(p<0.001\) in all cases.  
The Pearson correlation coefficients obtained can be found in Table 12.  
As observed, when counting the instances in which a translator's output demonstrates statistically significant differences or exhibits low or no correlation, the resulting data are presented in Table 13.  
According to the difference count, DeepL and Copilot stand out compared to the others. To determine whether this is because they are more similar to INCIBE's translations or because they deviate more, their results have been cross-checked by a ranking. This ranking was obtained by summarizing the evaluation metric results for each translation. If we look at the average score of each translator in Table 14, we could conclude that DeepL and Google Translate achieve the best overall performance considering all metrics, closely followed by ChatGPT.  
Upon a more detailed analysis of the alignment with INCIBE, it becomes evident from the ranking presented in Table 15 that translations produced by DeepL and Google Translate exhibit a closer alignment with the reference text, particularly in terms of structure and vocabulary. These tools also achieve the highest scores in both COMET and YiSi, suggesting that their translations are more fluent and semantically accurate. In terms of word order, all translators demonstrate relatively high values; however, ChatGPT and DeepL stand out as performing slightly better.  
Overall, DeepL and Google Translate emerge as the most effective tools, offering a balanced combination of lexical accuracy, fluency, and semantic fidelity. ChatGPT also delivers strong results, though it may exhibit minor deficiencies in word order and structural metrics. Bing, while yielding acceptable results, ranks lower than the top three translators. Copilot receives the lowest ratings across all evaluation metrics, indicating a lower degree of precision and fluency in its translations.  
Figure 2: Total number of wins in the word embeddings for each generative translater.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**ChatCPT vs.**} & \multicolumn{2}{c}{**ChatCPT vs.**} & \multicolumn{2}{c}{**ChatCPT vs.**} & \multicolumn{2}{c}{**ChatCPT vs.**} & \multicolumn{2}{c}{**GaleGT vs.**} & \multicolumn{2}{c}{**Bias vs.**} & \multicolumn{2}{c}{**Bing vs.**} & \multicolumn{2}{c}{**Bing vs.**} & \multicolumn{2}{c}{**Bing vs.**} & \multicolumn{2}{c}{**Deep1.vs.**} & \multicolumn{2}{c}{**Deep1.vs.**} \\  & \multicolumn{2}{c}{**Bing**} & \multicolumn{2}{c}{**DeepL**} & \multicolumn{2}{c}{**Congle**} & \multicolumn{2}{c}{**DeepL**} & \multicolumn{2}{c}{**Tanslate**} & \multicolumn{2}{c}{**Congle**} & \multicolumn{2}{c}{**Copilot**} & \multicolumn{2}{c}{**Tanslate**} & \multicolumn{2}{c}{**Copilot**} & \multicolumn{2}{c}{**Tanslate**} \\ \hline BLEU & \(p<0.001\) & \(p<0.001\) & \(p=0.001\) & \(p<0.