# A Mongolian-Chinese Neural Machine Translation Method Based on Semantic-Context Data Augmentation
## 4 Experiments
### Experimental Results and Analysis  
As shown in Table 2, the table presents the results of various methods in the Mongolian-Chinese translation task. From the data in the table, it can be concluded that the semantic-context data enhancement method proposed by us has a better translation effect on multiple different datasets than the existing DA strategies, such as back-translation [7], soft context data enhancement [4], SwitchOut [26], etc. The experimental results also show the importance of large-scale training data for MNMT. We observed that using simple DA, back-translation, and iterative back-translation DA methods and our proposed semantic-context DA method significantly improved translation quality. However, the method proposed in this paper generally achieved the best results and outperformed the other DA methods in the testing set. Because our method uses both the source and target sentence vectors in the translation process, we can obtain a \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model**} & **Initial** & **Easy Data** & **Back-** & **Iterative** \\  & **Corpus** & **Augmentation** & **Translation** & **Back-Translation** \\ \hline Transformer & 30.2 & 30.8 & 31.1 & 31.6 \\ \hline SwitchOut & 31.3 & 31.6 & 31.9 & 32.4 \\ \hline SemAug & 30.78 & 31.16 & 31.62 & 31.56 \\ \hline SCA & 31.02 & 31.17 & 32.03 & 32.17 \\ \hline
**Sem-NMT (ours)** & **32.73** & **33.15** & **33.57** & **34.55** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: BLEU of translation results. vector representation with rich semantic information. By using our method and the existing method in the pseudo-parallel corpus generated by different DA methods, it is obvious that with the expansion of the corpus scale, the translation effect is constantly improving. Due to its direct operation of sentences, simple data augmentation easily leads to issues of missing semantics. Compared with the back-translation and iterative back-translation methods, the translation results on the pseudo-parallel corpus generated by simple DA are poor. In general, the expansion of corpus size can effectively improve the quality of Mongolian-Chinese translation tasks. The main feature of Mongolian syntax is that the subject and object come before the predicate verb, and their order is not fixed. This syntactic structure often leads to structural confusion when translating Mongolian into other languages. As shown in Figure 6, several typical representative sentences were selected to translate Mongolian into Chinese using the Sem-NMT model. From the translation result of the first sentence, it can be seen that when translating ordinary sentences, the translated sentences are roughly the same as the reference translation. From the second translation result, it can be seen that passive sentences can easily be translated into content that is contrary to the original semantics in MNMT. This is because Mongolian grammar takes the subject-object-verb structure, while Chinese takes the subject-verb-object structure, which is not very conducive to translating complex sentences. In future research, the translation model also needs to be further improved to account for the corresponding grammatical structure of Mongolian and Chinese. Through the analysis of the translation results of Mongolian and Chinese, the translation model can effectively alleviate the problem of unknown words, and the translation effect of some place names and specific nouns is particularly prominent. Figure 7 show the changes in the loss and accuracy of the Sem-NMT model during training. From this graph, it can be seen that in the early stages of model training, the loss function decreases rapidly, and the accuracy of translation improves rapidly. With the increase in training time, the change in loss also increases, but the increase is small, and finally tends to stabilize. As the training time increases, the accuracy of model translation also continuously improves and tends to stabilize when reaching a certain value.