# A Chinese-Kazakh Translation Method That Combines Data Augmentation and R-Drop Regularization
## 4 Experiments
### Qualitative Analysis  
We randomly selected Chinese to Kazakh translation models and present a qualitative analysis in Table 4. It includes the source sentence, the reference translation, and the machine-generated translations from different NMT models.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Zh-Kk**} & \multicolumn{2}{c}{**Kk–Zh**} \\ \hline  & **BLEU** & **chrF++** & **BLEU** & **chrF++** \\ \hline Phrase-sub.+Rev. & 50.42 & 0.756 & 58.74 & 0.530 \\ \hline Phrase-sub.+Rev.+Token & 50.55 & 0.756 & 58.99 & 0.532 \\ \hline Phrase-sub.+Rev.+Swap & 51.46 & 0.761 & 58.58 & 0.527 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance of data augmentation methods on Zh-Kk and Kk–Zh tasks.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Zh–Kk**} & \multicolumn{4}{c}{**Kk–Zh**} \\ \hline \(\alpha\) & **BLEU** & **chrF++** & **Time** & **BLEU** & **chrF++** & **Time** \\ \hline
0.3 & 53.56 & 0.771 & 3.11h & 59.53 & 0.539 & 4.49h \\ \hline
0.4 & 54.46 & 0.777 & 4.48h & 60.15 & 0.544 & 5.46h \\ \hline
0.5 & 54.45 & 0.776 & 5.35h & 59.59 & 0.538 & 3.35h \\ \hline
0.6 & 54.29 & 0.774 & 5.96h & 59.28 & 0.535 & 3.05h \\ \hline
0.7 & 54.44 & 0.777 & 6.38h & 59.74 & 0.539 & 5.64h \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance with different \(\alpha\) values for fine-tuning on the Zh–Kk and Kk–Zh tasks.  
and strive to convey the original meaning. The mixed enhancement + R-Drop method may be the optimal choice, as it balances accuracy and conciseness.  
Table 5 shows a Kazakh source sentence and its corresponding Chinese reference translation. The table also includes machine-translated Chinese sentences generated by different NMT models. Notably, using the Mixed Enhancement+R-Drop approach produced more outstanding translations.  
The baseline translation has made simple modifications to the original sentence, replacing some vocabulary but retaining the meaning. However, it uses inaccurate terms, such as translating \" (\" (obstruction of traffic due to road occupation) as \" (road occupation, business traffic). In the phrase \" (\" (road occupation, business traffic), firstly, the verb \" (occupation) should come after the noun \" (road), resulting in \" (\" (ocup) the road). Therefore, the correct translation would be \"occupation of the road\". Secondly, considering the context, \" (business traffic) can be understood as traffic congestion caused by commercial activities. Hence, it should be translated as \"obstruction of traffic due to road occupation\".  
Phrase substitution improves the accuracy and fluency of the translation by replacing some phrases. In this case, \" (commercial promotion) is substituted with \" (promote through commere). Mixed Enhancement of various modification techniques, including phrase substitution, vocabulary deletion, and word order adjustment, achieves a better translation result. In addition to phrase substitution, \" (business) is changed to \" (commercial), making the translation more contextually appropriate.  
Mixed Enhancement + R-Drop Building upon mixed Enhancement: this method further omits some modifying words to simplify sentence structure and enhance conciseness. While this approach simplifies the translation, it may result in the loss of some detailed information.  
In summary, the different translation methods vary in their degree of processing and effectiveness for the original sentence. Phrase-substitution, Mixed Enhancement, and  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Method** & **Translations from Kazakh to Chinese** \\ \hline Source Sentence & \(\exists\)\(\exists\  
Mixed Enhancement + R-Drop improve the accuracy and fluency to a certain extent, while the baseline relatively contains some inaccuracies.