# A Chinese-Kazakh Translation Method That Combines Data Augmentation and R-Drop Regularization
## 4 Experiments
### Results and Discussion
#### 4.3.2 Result  
Table 1 presents the impact of various data augmentation methods on translation performance, measured in terms of BLEU and chrF++ scores. The following are the experimental results.  
For the Chinese to Kazakh translation task, we observed a BLEU score of 49.47 and a chrF++ score of 0.75 using the Transformer baseline model. However, when the phrase replacement data augmentation method was applied, we observed an improvement in the  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Zh–Kk** & & **Kk–Zh** \\ \hline  & **BLEU** & **chrF++** & **BLEU** & **chrF++** \\ \hline Transformer & 49.47 & 0.745 & 52.04 & 0.463 \\ \hline Back-translation & 49.93 & 0.746 & 54.21 & 0.478 \\ \hline Replace & 49.90 & 0.747 & 57.15 & 0.514 \\ \hline Token & 49.26 & 0.744 & 56.99 & 0.512 \\ \hline Swap & 48.91 & 0.742 & 57.15 & 0.513 \\ \hline Source & 48.93 & 0.742 & 52.14 & 0.462 \\ \hline Reverse & 49.81 & 0.750 & 57.85 & 0.516 \\ \hline Phrase-substitution & 50.15 & 0.752 & 57.35 & 0.514 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Performance of several data augmentation methods on Zh–Kk and Kk–Zh translation tasks with the SacreBLEU metric based on the transformer model.  
BLEU score to 50.15 and the chrF++ score to 0.76. This indicates that the phrase replacement data augmentation method has a significant performance improvement effect on the low-resource translation task from Chinese to Kazakh. Additionally, we observed that methods such as Token, Swap, and Source not only did not enhance the performance, but also impaired the translation quality. By applying the phrase replacement data augmentation method, more training samples can be generated, thereby increasing the amount and diversity of the training data for the model. More training data provides comprehensive information, enabling the model to learn better language representations and translation capabilities.  
For the translation task from Kazakh to Chinese, under the baseline Transformer model, it was observed that the BLEU score was 52.04 and the chrF++ score was 0.46. However, by applying the phrase replacement data augmentation method, it was observed that the BLEU score improved to 57.35, and the chrF++ score improved to 0.51. This once again demonstrates the effectiveness of the phrase replacement data augmentation method in low-resource translation tasks from Kazakh to Chinese. Furthermore, it was also observed that using the Reverse augmentation method resulted in a BLEU score increase to 57.85. Compared to other data augmentation methods, Reverse introduces more diverse language structures and contextual environments by reversing the Kazakh sentences. This helps the model better learn the correspondence between the source language and the target language, thereby improving translation accuracy and naturalness. Meanwhile, the Source augmentation method did not significantly improve machine translation performance for languages such as Chinese and Kazakh, which have rich morphological characteristics. Based on the above experimental results, it is recommended to consider using data augmentation methods such as phrase replacement and Reverse to further improve translation quality in the task of translating from Kazakh to Chinese. Additionally, depending on the specific task and data conditions, other data augmentation methods can be explored to discover better performance enhancement strategies.