# A Chinese-Kazakh Translation Method That Combines Data Augmentation and R-Drop Regularization
###### Abstract  
Low-resource languages often face the problem of insufficient data, which leads to poor quality in machine translation. One approach to address this issue is data augmentation. Data augmentation involves creating new data by transforming existing data through methods such as flipping, cropping, rotating, and adding noise. Traditionally, pseudo-parallel corpora are generated by randomly replacing words in low-resource language machine translation. However, this method can introduce ambiguity, as the same word may have different meanings in different contexts. This study proposes a new approach for low-resource language machine translation, which involves generating pseudo-parallel corpora by replacing phrases. The performance of this approach is compared with other data augmentation methods, and it is observed that combining it with other data augmentation methods further improves performance. To enhance the robustness of the model, R-Drop regularization is also used. R-Drop is an effective method for improving the quality of machine translation. The proposed method was tested on Chinese-Kazakh (Arabic script) translation tasks, resulting in performance improvements of 4.99 and 7.7 for Chinese-to-Kazakh and Kazakh-to-Chinese translations, respectively. By combining the generation of pseudo-parallel corpora through phrase replacement with the application of R-Drop regularization, there is a significant advancement in machine translation performance for low-resource languages. machine translation; data augmentation; phrase replacement; R-Drop +
Footnote †: journal: Medical  
a variety of techniques, such as synonym replacement, sentence rearrangement, word insertion and deletion, and back-translation [13; 14; 15; 16; 17; 18]. By leveraging these techniques, the original dataset can be expanded to provide more training samples, thereby improving machine translation performance under low-resource conditions. However, data augmentation techniques have some limitations [19], such as the possibility of generating unnatural translations, which may affect the accuracy of the translation. Synonym replacement involves replacing certain words in the original sentence with their synonyms to generate new sentence pairs, thereby increasing data diversity [13; 14]. Sentence shuffling rearranges the word order in the original sentence to create sentence pairs with the same semantics but different structures, helping the model learn different sentence structures and grammar rules. Word insertion and deletion can insert or remove certain words in the original sentence, generating slightly modified sentence pairs to increase data richness and diversity [15]. Back-translation involves first training an intermediate system with parallel data and then using that system to translate monolingual data in the target language back into the source language [16; 17; 18]. Although back-translation has been widely used in data augmentation, it may encounter issues such as translation errors and fluency due to the limitations of the machine translation model [20]. These problems can lead to significant differences between the generated pseudo-parallel data and real parallel data. Our plan is to augment parallel corpora using phrase replacement and increase data diversity, while reducing noise through various data augmentation methods. Additionally, we introduce the R-Drop regularization method to improve the robustness of the model [21]. With these measures, we aim to train an outstanding Chinese-Kazakh machine translation model. The contributions of this work can be summarized as follows:  
1. Parallel corpus augmentation: We successfully expanded the parallel corpus required for the Chinese-Kazakh machine translation task by utilizing phrase replacement techniques. By introducing more variations and diversities, we increased the richness of the training data, providing more information and context for training machine translation models. 2.