# A Chinese-Kazakh Translation Method That Combines Data Augmentation and R-Drop Regularization
## 3 Method
### Generating a High-Quality Phrase Table  
The Moses system is a phrase-based statistical machine translation system that utilizes a phrase table to establish translation correspondences between the source language and the target language [36]. The translation process in Moses is conducted through a decoding procedure. The phrase table stores phrase pairs and their translation probabilities, learned from parallel corpora. In the Moses system, the phrase table is considered one of the core components. It is a data structure that stores the correspondence between source language phrases and target language phrases. The phrase table contains phrase pairs and their associated translation probabilities, which indicate the likelihood of translation between the source and target phrases. These probabilities are estimated based on the training data set and statistical algorithms, often using maximum likelihood estimation. By utilizing the information stored in the phrase table during the decoding process, the Moses system can select the most appropriate translations for the input source sentences. The translation probabilities in the phrase table play a crucial role in determining the quality and accuracy of the translation output. Continuous refinement and augmentation of the phrase table through additional training data can improve the performance of the Moses system. Each phrase pair has five features:  
* The phrase translation probability \(\varphi(\mathrm{f}\,\mathrm{e})\);
* The lexical weighting \(\mathrm{lex}(\mathrm{f}\,\mathrm{e})\);
* The phrase inverse translation probability \(\varphi(\mathrm{e}\,\mathrm{f})\);  
Figure 1: Illustration of our proposed method.  
* The inverse lexical weighting lex(e! f);
* The phrase penalty, currently always e = 2.718.  
The first four features have probabilistic values ranging from zero to one, while the fifth feature remains constant.  
In order to reduce noise in the phrase table, pruning is applied. When performing phrase table pruning, a threshold-based method can be used. The threshold is a predefined value that retains only those phrase pairs with probabilities higher than the threshold while removing phrase pairs with probabilities lower than the threshold. The benefit of using a threshold for phrase table pruning is filtering out phrase pairs with very low probabilities, as these are often unlikely to occur or irrelevant to the task. By removing these less important phrase pairs, the size of the phrase table can be significantly reduced while improving its quality and effectiveness [37].  
To further enhance the quality of the phrase table, we can use part-of-speech (POS) filtering rules for refinement. POS filtering means selectively retaining or removing phrases based on specific POS tags. Since there is no POS tagging tool for Xinjiang Kazakh, we use Jieba to assign POS tags to each word in the Chinese phrases. Then, based on specific requirements, we retain or remove phrases with certain POS tags. We judge the POS tag of the last word in the Chinese phrase. Common POS tags include nouns (NN), verbs (VB), adjectives (JJ), and adverbs (RB). These POS tags often contain important semantic information and are thus retained in the phrases. On the other hand, POS tags such as determiners (DT) (such as \"some\", \"every\", and \"any\"), pronouns (PRP) (such as \"you\", \"I\", \"she\"), and prepositions (IN) often carry little semantic information but are abundant in the phrase table. By deleting these low-semantic Chinese phrases and their corresponding Kazakh phrases, we can effectively reduce redundancy and improve the quality of the phrase table. Since many irrelevant phrase pairs are deleted, the remaining phrase pairs are more similar in structure, reducing the noise introduced by the pseudo data generated by phrase substitution.